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1 | Title | Authors | Keywords | Abstract |
2 | Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data | Zeller, Matthias, CARIAD SE Behley, Jens, University of Bonn Heidingsfeld, Michael, CARIAD SE Stachniss, Cyrill, University of Bonn | Keywords: Semantic Scene Understanding, Deep Learning Methods | Abstract: Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full potential of multi-modal sensor suites, radar sensors are essential for safety critical tasks and are already installed in most new vehicles today. In this paper, we address the problem of semantic segmentation of moving objects in radar point clouds to enhance the perception of the environment with another sensor modality. Instead of aggregating multiple scans to densify the point clouds, we propose a novel approach based on the self-attention mechanism to accurately perform sparse, single-scan segmentation. Our approach, called Gaussian Radar Transformer, includes the newly introduced Gaussian transformer layer, which replaces the softmax normalization by a Gaussian function to decouple the contribution of individual points. To tackle the challenge of the transformer to capture long-range dependencies, we propose our attentive up- and downsampling modules to enlarge the receptive field and capture strong spatial relations. We compare our approach to other state-of-the-art methods on the RadarScenes data set and show superior segmentation quality in diverse environments, even without exploiting temporal information. |
3 | Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving | Marcuzzi, Rodrigo, University of Bonn Nunes, Lucas, University of Bonn Wiesmann, Louis, University of Bonn Behley, Jens, University of Bonn Stachniss, Cyrill, University of Bonn | Keywords: Semantic Scene Understanding, Deep Learning Methods | Abstract: Autonomous vehicles need to understand their surroundings geometrically and semantically to plan and act appropriately in the real world. Panoptic segmentation of LiDAR scans provides a description of the surroundings by unifying semantic and instance segmentation. It is usually solved in a bottom-up manner, consisting of two steps. Predicting the semantic class for 3D each point, using this information to filter out ”stuff” points, and cluster the ”thing” points to obtain instance segmentation. The clustering is a post-processing step that often needs hyperparameter tuning, which usually does not adapt to instances of different sizes or different datasets. To this end, we propose MaskPLS, an approach to perform panoptic segmentation of LiDAR scans in an end-to-end manner by predicting a set of non-overlapping binary masks and semantic classes, fully avoiding the clustering step. As a result, each mask represents a single instance belonging to a ”thing” class or a complete ”stuff” class. Experiments on SemanticKITTI show that the end-to-end learnable mask generation leads to superior performance compared to state-of-the-art heuristic approaches. |
4 | SCENE: Reasoning about Traffic Scenes Using Heterogeneous Graph Neural Networks | Schmidt, Julian, Mercedes-Benz AG, Ulm University Monninger, Thomas, Mercedes-Benz AG, University of Stuttgart Rupprecht, Jan, Mercedes-Benz AG Raba, David, Mercedes Benz AG Jordan, Julian, Mercedes-Benz AG Frank, Daniel, University of Stuttgart Staab, Steffen, University of Stuttgart Dietmayer, Klaus, University of Ulm | Keywords: Semantic Scene Understanding, AI-Based Methods, Behavior-Based Systems | Abstract: Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains. |
5 | Prototypical Contrastive Transfer Learning for Multimodal Language Understanding | Otsuki, Seitaro, Keio University Ishikawa, Shintaro, Keio University Sugiura, Komei, Keio University | Keywords: Transfer Learning, Semantic Scene Understanding, Multi-Modal Perception for HRI | Abstract: Although domestic service robots are expected to assist individuals who require support, they cannot currently interact smoothly with people through natural language. For example, given the instruction "Bring me a bottle from the kitchen," it is difficult for such robots to specify the bottle in an indoor environment. Most conventional models have been trained on real-world datasets that are labor-intensive to collect, and they have not fully leveraged simulation data through a transfer learning framework. In this study, we propose a novel transfer learning approach for multimodal language understanding called Prototypical Contrastive Transfer Learning (PCTL), which uses a new contrastive loss called Dual ProtoNCE. We introduce PCTL to the task of identifying target objects in domestic environments according to free-form natural language instructions. To validate PCTL, we built new real-world and simulation datasets. Our experiment demonstrated that PCTL outperformed existing methods. Specifically, PCTL achieved an accuracy of 78.1%, whereas simple fine-tuning achieved an accuracy of 73.4%. |
6 | Re-Thinking Classification Confidence with Model Quality Quantification | Pan, Yancheng, Peking University Zhao, Huijing, Peking University | Keywords: Semantic Scene Understanding, Autonomous Agents | Abstract: Deep neural networks using for real-world classification task require high reliability and robustness. However, the Softmax output by the last layer of network is often over-confident. We propose a novel confidence estimation method by considering model quality for deep classification models. Two metrics, MQ-Repres and MQ-Discri are developed accordingly to evaluate the model quality, and also provide a new confidence estimation called MQ-Conf for online inference. We demonstrate the capability of the proposed method by the 3D semantic segmentation tasks using three different deep networks. Through confusion analysis and feature visualization we show the rationality and reliability of the model quality quantification method. |
7 | Self-Supervised Drivable Area Segmentation Using LiDAR’s Depth Information for Autonomous Driving | Ma, Fulong, The Hong Kong University of Science and Technology Liu, Yang, The Hong Kong University of Science and Technology Wang, Sheng, Hong Kong University of Science and Technology Jin, Wu, UESTC Qi, Weiqing, HKUST Liu, Ming, Hong Kong University of Science and Technology | Keywords: Semantic Scene Understanding, Perception for Grasping and Manipulation, Mapping | Abstract: Drivable area segmentation is an essential component of the visual perception system for autonomous driving vehicles. Recent efforts in deep neural networks have significantly improved semantic segmentation performance for autonomous driving. However, most DNN-based methods need a large amount of data to train the models, and collecting large-scale datasets with manually labeled ground truth is costly, tedious, time consuming and requires the availability of experts, making DNN-based methods often difficult to implement in real world applications. Hence, in this paper, we introduce a novel module named automatic data labeler (ADL), which leverages a deterministic LiDAR-based method for ground plane segmentation and road boundary detection to create large datasets suitable for training DNNs. Furthermore, since the data generated by our ADL module is not as accurate as the manually annotated data, we introduce uncertainty estimation to compensate for the gap between the human labeler and our ADL. Finally, we train the semantic segmentation neural networks using our automatically generated labels on the KITTI dataset and KITTI-CARLA dataset. The experimental results demonstrate that our proposed ADL method not only achieves impressive performance compared to manual labeling but also exhibits more robust and accurate results than both traditional methods and state-of-the-art self-supervised methods. |
8 | Vehicle Motion Forecasting Using Prior Information and Semantic-Assisted Occupancy Grid Maps | Asghar, Rabbia, INRIA / Univ. Grenoble Alpes Diaz-Zapata, Manuel, Inria Grenoble Rummelhard, Lukas, INRIA Spalanzani, Anne, INRIA / Univ. Grenoble Alpes Laugier, Christian, INRIA | Keywords: Semantic Scene Understanding, Deep Learning Methods, Autonomous Vehicle Navigation | Abstract: Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic labels to the occupied cells and incorporating map information. We propose a novel framework that combines deep-learning-based spatio-temporal and probabilistic approaches to predict multimodal vehicle behaviors. Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset and show that our model shows superior ability to predict both static and dynamic vehicles compared to OGM predictions. Furthermore, we perform an ablation study and assess the role of semantic labels and map in the architecture. |
9 | Enhance Local Feature Consistency with Structure Similarity Loss for 3D Semantic Segmentation | Lin, Cheng-Wei, Department of Computer Science, National Yang Ming Chiao Tung Un Syu, Fang-Yu, Department of Computer Science, National Yang Ming Chiao Tung Un Pan, Yi-Ju, National Yang Ming Chiao Tung University Chen, Kuan-Wen, National Yang Ming Chiao Tung University | Keywords: Semantic Scene Understanding, Object Detection, Segmentation and Categorization, Deep Learning for Visual Perception | Abstract: Recently, many research studies have been carried out on using deep learning methods for 3D point cloud understanding. However, there is still no remarkable result on 3D point cloud semantic segmentation compared to those of 2D research. One important reason is that 3D data has higher dimensionality but lacks large datasets, which means that the deep learning model is difficult to optimize and easy to overfit. To overcome this, an essential method is to provide more priors to the learning of deep models. In this paper, we focus on semantic segmentation for point clouds in the real world. To provide priors to the model, we propose a novel loss function called Linearity and Planarity to enhance local feature consistency in the regions with similar local structure. Experiments show that the proposed method improves baseline performance on both indoor and outdoor datasets e.g. S3DIS and Semantic3D. |
10 | Lightweight Semantic Segmentation Network for Semantic Scene Understanding on Low-Compute Devices | Son, Hojun, University of Michigan Weiland, James, University of Michigan | Keywords: Semantic Scene Understanding, Embedded Systems for Robotic and Automation, Deep Learning for Visual Perception | Abstract: Semantic scene understanding is beneficial for mobile robots. Semantic information obtained through onboard cameras can improve robots’ navigation performance. However, obtaining semantic information on small mobile robots with constrained power and computation resources is challenging. We propose a new lightweight convolution neural network comparable to previous semantic segmentation algorithms for mobile applications. Our network achieved 73.06% on the Cityscapes validation set and 71.8% on the Cityscapes test set. Our model runs at 116 FPS with 1024x2048, 172 fps with 1024x1024, and 175 FPS with 720x960 on NVIDIA GTX 1080. We analyze a model size, which is defined as the summation of the number of floating operations and the number of parameters. The smaller model size enables tiny mobile robot systems that should operate multiple tasks simultaneously to work efficiently. Our model has the smallest model size compared to the real-time semantic segmentation convolution neural networks ranked on Cityscapes real-time benchmark and other high-performing, lightweight convolution neural networks. On the Camvid test set, our model achieved a mIoU of 73.29% with Cityscapes pre-training, which outperformed the accuracy of other lightweight convolution neural networks. For mobile applicability, we measured frame-per-second on different low-compute devices. Our model operates 35 FPS on Jetson Xavier AGX, 21 FPS on Jetson Xavier NX, and 14 FPS on a ROS ASUS gaming phone. 1024x2048 resolution is used for the Jetson devices, and 512x512 size is utilized for the measurement on the phone. Our network did not use extra datasets such as ImageNet, Coarse Cityscapes, and Mapillary. Additionally, we did not use TensorRT to achieve fast inference speed. Compared to other real-time and lightweight CNNs, our model achieved significantly more efficiency while balancing accuracy, inference speed, and model size. |
11 | LiDAR-SGMOS: Semantics-Guided Moving Object Segmentation with 3D LiDAR | Gu, Shuo, Nanjing University of Science and Technology Yao, Suling, Nanjing University of Science and Technology Yang, Jian, Nanjing University of Science & Technology Xu, Chengzhong, University of Macau Kong, Hui, University of Macau | Keywords: Semantic Scene Understanding, Object Detection, Segmentation and Categorization, Deep Learning Methods | Abstract: Most of the existing moving object segmentation (MOS) methods regard MOS as an independent task, in this paper, we associate the MOS task with semantic segmentation, and propose a semantics-guided network for moving object segmentation (LiDAR-SGMOS). We first transform the range image and semantic features of the past scan into the range view of current scan based on the relative pose between scans. The residual image is obtained by calculating the normalized absolute difference between the current and transformed range images. Then, we apply a Meta-Kernel based cross scan fusion (CSF) module to adaptively fuse the range images and semantic features of current scan, the residual image and transformed features. Finally, the fused features with rich motion and semantic information are processed to obtain reliable MOS results. We also introduce a residual image augmentation method to further improve the MOS performance. Our method outperforms most LiDAR-MOS methods with only two sequential LiDAR scans as inputs on the SemanticKITTI MOS dataset. |
12 | Robust Fusion for Bayesian Semantic Mapping | Morilla-Cabello, David, Universidad De Zaragoza Mur Labadia, Lorenzo, University of Zaragoza Martinez-Cantin, Ruben, University of Zaragoza Montijano, Eduardo, Universidad De Zaragoza | Keywords: Semantic Scene Understanding, Mapping, Deep Learning for Visual Perception | Abstract: The integration of semantic information in a map allows robots to understand better their environment and make high-level decisions. In the last few years, neural networks have shown enormous progress in their perception capabilities. However, when fusing multiple observations from a neural network in a semantic map, its inherent overconfidence with unknown data gives too much weight to the outliers and decreases the robustness. To mitigate this issue we propose a novel robust fusion method to combine multiple Bayesian semantic predictions. Our method uses the uncertainty estimation provided by a Bayesian neural network to calibrate the way in which the measurements are fused. This is done by regularizing the observations to mitigate the problem of overconfident outlier predictions and using the epistemic uncertainty to weigh their influence in the fusion, resulting in a different formulation of the probability distributions. We validate our robust fusion strategy by performing experiments on photo-realistic simulated environments and real scenes. In both cases, we use a network trained on different data to expose the model to varying data distributions. The results show that considering the model's uncertainty and regularizing the probability distribution of the observations distribution results in a better semantic segmentation performance and more robustness to outliers, compared with other methods. |
13 | ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes | Ramachandruni, Kartik, Georgia Institute of Technology Zuo, Max, Georgia Institute of Technology Chernova, Sonia, Georgia Institute of Technology | Keywords: Semantic Scene Understanding, Deep Learning Methods | Abstract: Object rearrangement is the problem of enabling a robot to identify the correct object placement in a complex environment. Prior work on object rearrangement has explored a diverse set of techniques for following user instructions to achieve some desired goal state. Logical predicates, images of the goal scene, and natural language descriptions have all been used to instruct a robot in how to arrange objects. In this work, we argue that burdening the user with specifying goal scenes is not necessary in partially-arranged environments, such as common household settings. Instead, we show that contextual cues from partially arranged scenes (i.e., the placement of some number of pre-arranged objects in the environment) provide sufficient context to enable robots to perform object rearrangement without any explicit user goal specification. We introduce ConSOR, a Context-aware Semantic Object Rearrangement framework that utilizes contextual cues from a partially arranged initial state of the environment to complete the arrangement of new objects, without explicit goal specification from the user. We demonstrate that ConSOR strongly outperforms two baselines in generalizing to novel object arrangements and unseen object categories. The code and data are available at https://github.com/kartikvrama/consor. |
14 | IDA: Informed Domain Adaptive Semantic Segmentation | Chen, Zheng, Indiana University Bloomington Ding, Zhengming, Tulane University Gregory, Jason M., US Army Research Laboratory Liu, Lantao, Indiana University | Keywords: Semantic Scene Understanding, Deep Learning for Visual Perception, Object Detection, Segmentation and Categorization | Abstract: Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unfortunately ignores the dynamic evolution of different semantics across various domains as training proceeds. To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup. In our IDA model, the class-level performance is tracked by an expected confidence score (ECS). We then use a dynamic schedule to determine the mixing ratio for data in different domains. Extensive experimental results reveal that our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in the adaptation of SYNTHIA to Cityscapes. |
15 | Self-Supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species | Siddique, Abubakar, Marquette University Tabb, Amy, USDA-ARS-AFRS Medeiros, Henry, University of Florida | Keywords: Semantic Scene Understanding, Object Detection, Segmentation and Categorization, Incremental Learning | Abstract: Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications. |
16 | Combined Admittance Control with Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives (I) | Escarabajal, Rafael J., Universidad Politécnica De Valencia Pulloquinga, José Luis, Universidad Politécnica De Valencia Valera, Angel, Universidad Politécnica De Valencia Mata, Vicente, Universidad Politécnica De Valencia Valles, Marina, Universitat Politècnica De València Castillo-García, Fernando J., Universidad De Castilla-La Mancha | Keywords: Rehabilitation Robotics, Parallel Robots, Compliance and Impedance Control, Dynamic Movement Primitives | Abstract: This paper addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human-robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: i) the force exerted on the human must be controlled and limited, and ii) Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the Dynamic Movement Primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises. |
17 | A Handle Robot for Providing Bodily Support to Elderly Persons | Bolli, Roberto, MIT Bonato, Paolo, Harvard Medical School Asada, Harry, MIT | Keywords: Physically Assistive Devices, Human-Robot Collaboration, Domestic Robotics | Abstract: Age-related loss of mobility and an increased risk of falling remain major obstacles for older adults to live independently. Many elderly people lack the coordination and strength necessary to perform activities of daily living, such as getting out of bed or stepping into a bathtub. A traditional solution is to install grab bars around the home. For assisting in bathtub transitions, grab bars are fixed to a bathroom wall. However, they are often too far to reach and stably support the user; the installation locations of grab bars are constrained by the room layout and are often suboptimal. In this paper, we present a mobile robot that provides an older adult with a handlebar located anywhere in space - “Handle Anywhere”. The robot consists of an omnidirectional mobile base attached to a repositionable handlebar. We further develop a methodology to optimally place the handle to provide the maximum support for the elderly user while performing common postural changes. A cost function with a trade-off between mechanical advantage and manipulability of the user’s arm was optimized in terms of the location of the handlebar relative to the user. The methodology requires only a sagittal plane video of the elderly user performing the postural change, and thus is rapid, scalable, and uniquely customizable to each user. A proof-of-concept prototype was built, and the optimization algorithm for handle location was validated experimentally. |
18 | A Hybrid FNS Generator for Human Trunk Posture Control with Incomplete Knowledge of Neuromusculoskeletal Dynamics | Bao, Xuefeng, Case Western Reserve University Friederich, Aidan, Case Western Reserve University Triolo, Ronald, Case Western Reserve University Audu, Musa. L., Case Western Reserve University | Keywords: Rehabilitation Robotics, Modeling and Simulating Humans, Motion Control | Abstract: The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by Functional Neuromuscular Stimulation (FNS), a technique that applies low-level current to motor nerves to activate the muscles generating torques, and thus, produce trunk motions. FNS can be modulated to control trunk movements. However, a stabilizing modulation policy (i.e., control law) is difficult to derive due to the complexity of neuromusculoskeletal dynamics, which consist of skeletal dynamics (i.e., multi-joint rigid body dynamics) and neuromuscular dynamics (i.e., a highly nonlinear, nonautonomous, and input redundant dynamics). Therefore, an FNS-based control method that can stabilize the trunk without knowing the accurate skeletal and neuromuscular dynamics is desired. This work proposed an FNS generator, which consists of a robust nonlinear controller (RNC) that provides stabilizing torque command and an artificial neural network (ANN)- based torque-to-activation (T-A) map to ensure that the muscle generates the stabilizing torque to the skeleton. Due to the robustness and learning capability of this control framework, full knowledge of the trunk neuromusculoskeletal dynamics is not required. The proposed control framework has been tested in a simulation environment where an anatomically realistic 3D musculoskeletal model of the human trunk was manipulated to follow a time-varying reference that moves in the anterior-posterior and medial-lateral directions. From the results, it can be seen that the trunk motion converges to a satisfactory trajectory while the ANN is being updated. The results suggest the potential of this control framework for trunk tracking tasks in a clinical application. |
19 | Insole-Type Walking Assist Device Capable of Inducing Inversion-Eversion of the Ankle Angle to the Neutral Position | Itami, Taku, Aoyama Gakuin University Date, Kazuki, Aoyama Gakuin University Ishii, Yuuta, Aoyama Gakuin University Yoneyama, Jun, Aoyama Gakuin University Aoki, Takaaki, Gifu University | Keywords: Prosthetics and Exoskeletons, Robotics and Automation in Life Sciences, Body Balancing | Abstract: In recent years, the aging of society has become a serious problem, especially in developed countries. Walking is an important element in extending healthy life expectancy in old age. In particular, induction of proper ankle joint alignment at heel contact is important during the gait cycle from the perspective of smooth weight transfer and reduction of burden on the knees and hip. In this study, we focus on the behavior of the ankle joint at heel contact and propose an insole-type assist device that can induce the ankle angle inversion/eversion rotation. The proposed device has tilting of the heel part from left to right in response to the rotation of a stepping motor, and an inertial sensor mounted inside controls the heel part to always maintain a horizontal position. The effectiveness of the proposed device is verified by evaluating the amount of lateral thrust of the knee joint of six healthy male subjects during a foot-stepping motion using motion capture system. The results showed that the amount of lateral thrust is significantly reduced by wearing the device with control. |
20 | Design for Hip Abduction Assistive Device Based on Relationship between Hip Joint Motion and Torque During Running | Lee, Myunghyun, Agency for Defense Development Hong, Man Bok, Agency for Defense Development Kim, Gwang Tae, Agency for Defense Development Kim, Seonwoo, Agency for Defense Development | Keywords: Physically Assistive Devices, Human Performance Augmentation, Mechanism Design | Abstract: Numerous attempts have been made to reduce metabolic energy while running with the help of assistive devices. A majority of studies on the assistive devices have focused on the assisting torque in the sagittal plane. In the case of running, however, the abduction torque in the frontal plane at the hip joint is greater than the flexion/extension torque in the sagittal plane. During running, as does an elastic body, the abduction torque and the motion of the hip joint have a linear relationship, but are opposite in direction. It is expected that the hip abduction torque can be assisted with a simple passive method by using an elastic body that reflects the movement characteristics of the hip joint. In this study, therefore, a system to assist hip abduction torque using a leaf spring was proposed with a prototype testing. While running with the assist system proposed, the leaf spring aids the abduction torque on the stance phase, and the torque is not generated due to the passive revolute joint on the swing phase. The joint angle is changed with respective to the rotation in the flexion/extension direction to prevent discomfort torque during swing phase and to increase the duration of the torque action during stance phase. A preliminary test was conducted on one subject using the prototype of the hip joint abduction torque assistive device. The participant with the assistive device reduced metabolic energy by 5% compared to the case without abduction torque assist while running at 2.5m/s. In order to increase the amount of metabolic reduction, the device shall be supplemented by system mass reduction and hip joint position optimization. |
21 | Dynamic Hand Proprioception Via a Wearable Glove with Fabric Sensors | Behnke, Lily, Yale University Sanchez-Botero, Lina, Yale University Johnson, William, Yale University Agrawala, Anjali, Yale University Kramer-Bottiglio, Rebecca, Yale University | Keywords: Wearable Robotics, Soft Sensors and Actuators, Soft Robot Materials and Design | Abstract: Continuous enhancement in wearable technologies has led to several innovations in the healthcare, virtual reality, and robotics sectors. One form of wearable technology is wearable sensors for kinematic measurements of human motion. However, measuring the kinematics of human movement is a challenging problem as wearable sensors need to conform to complex curvatures and deform without limiting the user's natural range of motion. In fine motor activities, such challenges are further exacerbated by the dense packing of several joints, coupled joint motions, and relatively small deformations. This work presents the design, fabrication, and characterization of a thin, breathable sensing glove capable of reconstructing fine motor kinematics. The fabric glove features capacitive sensors made from layers of conductive and dielectric fabrics, culminating in a non-bulky and discrete glove design. This study demonstrates that the glove can reconstruct the joint angles of the wearer with a root mean square error of 7.2 degrees, indicating promising applicability to dynamic pose reconstruction for wearable technology and robot teleoperation. |
22 | A Wearable Robotic Rehabilitation System for Neuro-Rehabilitation Aimed at Enhancing Mediolateral Balance | Yu, Zhenyuan, North Carolina State University Nalam, Varun, North Carolina State University Alili, Abbas, NC State University Huang, He (Helen), North Carolina State University and University of North Carolina | Keywords: Rehabilitation Robotics, Prosthetics and Exoskeletons, Physical Human-Robot Interaction | Abstract: There is increasing evidence of the role of compromised mediolateral balance in falls and the need for rehabilitation specifically focused on mediolateral direction for various populations with motor deficits. To address this need, we have developed a neurorehabilitation platform by integrating a wearable robotic hip abduction-adduction exoskeleton with a visual interface. The platform is expected to influence and rehabilitate the underlying visuomotor mechanisms in individuals by having users perform motion tasks based on visual feedback while the robot applies various controlled resistances governed by the admittance controller implemented in the robot. A preliminary study was performed on 3 non disabled individuals to analyze the performance of the system and observe any adaptation in hip joint kinematics and kinetics as a result of the visuomotor training under 4 different admittance conditions. All three subjects exhibited increased consistency of motion during training and interlimb coordination to achieve motion tasks, demonstrating the utility of the system. Further analysis of observed human-robot torque interactions and electromyography (EMG) signals, and its implication in neurorehabilitation aimed at populations suffering from chronic stroke are discussed. |
23 | Analysis of Lower Extremity Shape Characteristics in Various Walking Situations for the Development of Wearable Robot | Park, Joohyun, KAIST, KIST Choi, Ho Seon, Yonsei University In, HyunKi, Korea Institute of Science and Technology | Keywords: Datasets for Human Motion, Wearable Robotics, Physical Human-Robot Interaction | Abstract: A strap is a frequently utilized component for securing wearable robots to their users in order to facilitate force transmission between humans and the devices. For the appropriate function of the wearable robot, the pressure between the strap and the skin should be maintained appropriately. Due to muscle contraction, the cross-section area of the human limb changes according to the movement of the muscle. The cross-section area change causes the change in the pressure applied by the strap. Therefore, for a new strap design to resolve this, it is necessary to understand the shape change characteristics of the muscle where the strap is applied. In this paper, the change in the circumference of the thigh and the calf during walking was measured and analyzed by multiple string pot sensors. With a treadmill and string pot sensors using potentiometers, torsion springs, and leg circumference changes were measured for different walking speeds and slopes. And, gait cycles were divided according to a signal from the FSR sensor inserted in the right shoe. From the experimental results, there were changes in the circumference of about 8.5mm and 3mm for the thigh and the calf, respectively. And we found tendencies in various walking circumstances such as walking speed and degree of the slope. It is confirmed that they can be used for estimation algorithms of gait cycles or gait circumstances. |
24 | Finding Biomechanically Safe Trajectories for Robot Manipulation of the Human Body in a Search and Rescue Scenario | Peiros, Lizzie, University of California, San Diego Chiu, Zih-Yun, University of California, San Diego Zhi, Yuheng, University of California, San Diego Shinde, Nikhil, University of California San Diego Yip, Michael C., University of California, San Diego | Keywords: Physical Human-Robot Interaction, Modeling and Simulating Humans, Dynamics | Abstract: There has been increasing awareness of the difficulties in reaching and extracting people from mass casualty scenarios, such as those arising from natural disasters. While platforms have been designed to consider reaching casualties and even carrying them out of harm's way, the challenge of physically repositioning a casualty from its found configuration to one suitable for extraction has not been explicitly explored. Furthermore, this type of planning problem needs to incorporate biomechanical safety considerations for the casualty. Thus, we present the problem formulation for biomechanically safe trajectory generation for repositioning limbs of unconscious human casualties. We describe biomechanical safety in robotics terms, describe mechanical descriptions of the dynamics of the robot-human coupled system, and the planning and trajectory optimization process that considers this coupled and constrained system. We finally evaluate the work over several variations of the problem and provide a live example. This work provides a crucial part of search and rescue that can be used in conjunction with past and present works involving robots and vision systems designed for search and rescue. |
25 | Mechanical Characterisation of Woven Pneumatic Active Textile | Marshall, Ruby, The University of Edinburgh Souppez, Jean-Baptiste, Aston University Khan, Mariya, Aston University Viola, Ignazio Maria, University of Edinburgh Nabae, Hiroyuki, Tokyo Institute of Technology Suzumori, Koichi, Tokyo Institute of Technology Stokes, Adam Andrew, University of Edinburgh Giorgio-Serchi, Francesco, University of Edinburgh | Keywords: Wearable Robotics, Soft Robot Materials and Design, Hydraulic/Pneumatic Actuators | Abstract: Active textiles have shown promising applications in soft robotics owing to their tunable stiffness and design flexibility. Given the breadth of the design space for planar and spatial arrangements of these woven structures, a rig- orous and generalizable characterisation of these systems is not yet available. In order to characterize the response of a stereotypical woven pattern to actuation, we undertake a parametric study of plain weave active fabrics and characterise their mechanical properties in accordance with the relevant ISO standards for varying muscle densities and both monotonically increasing/decreasing pressures. Tensile and flexural tests were undertaken on five plain weave samples made of a nylon 6 (polyamide) warp and EM20 McKibben S-muscle weft, for input pressures ranging from 0.00 MPa to 0.60 MPa, at three muscle densities, namely 100 m^-1, 74.26 m^-1 and 47.62 m^-1. Contrary to intuition, we find that a lower muscle density has a more prominent impact on the thickness, but a significantly lesser one on length, highlighting a critical dependency on the relative orientation among the loading, the passive textile and the muscle filaments. Hysteretic behaviour as large as 10% of the longitudinal contraction is observed on individual filaments and woven textiles, and its onset is identified in the shear between the rubber tube and the outer sleeve of the artificial muscle. Hysteresis is shown to be muscle density-dependent and responsible for a strongly asymmetrical response upon different pressure inputs. These findings provide new insights into the mechanical properties of active textiles with tunable stiffness, and may contribute to future developments in wearable technologies and biomedical devices. |
26 | Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis | Liu, Rongkai, University of Science and Technology of China（USTC） Ma, Tingting, Chinese Academy of Sciences Yao, Ningguang, University of Science and Technology of China Li, Hao, Chinese Academy of Sciences Zhao, Xinyan, University of Science and Technology of China Wang, Yu, University of Science and Technology of China Pan, Hongqing, Hefei Institutes of Physical Science Song, Quanjun, Chinese Academy of Science | Keywords: Human-Centered Robotics, Rehabilitation Robotics, Human-Robot Collaboration | Abstract: Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients and robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assist AKO. |
27 | Data-Driven Modeling for Gait Phase Recognition in a Wearable Exoskeleton Using Estimated Forces (I) | Park, Kyeong-Won, Republic of Korea Air Force Academy Choi, Jungsu, Yeungnam University Kong, Kyoungchul, Korea Advanced Institute of Science and Technology | Keywords: Wearable Robots, AI-Based Methods, Human-Centered Robotics, Robust/Adaptive Control of Robotic Systems | Abstract: Accurate identification of gait phases is critical in effectively assessing the assistance provided by lower-limb exoskeletons. In this study, we propose a novel gait phase recognition system called ObsNet to analyze the gait of individuals with spinal cord injuries (SCI). To ensure the reliable use of exoskeletons, it is essential to maintain practicality and avoid exposing the system to unnecessary risks of fatigue, inaccuracy, or incompatibility with human-centered devices. Therefore, we propose a new approach to characterize exoskeletal-assisted gait by estimating forces on exoskeletal joints during walking. Although these estimated forces are potentially useful for detecting gait phases, their nonlinearities make it challenging for existing algorithms to generalize accurately. To address this challenge, we introduce a data-driven model that simultaneously captures both feature extraction and order dependencies, and enhance its performance through a threshold-based compensational method to filter out momentary errors. We evaluated the effectiveness of ObsNet through robotic walking experiments with two practical users with complete paraplegia. Our results indicate that ObsNet outperformed state-of-the-art methods that use joint information and other recurrent networks in identifying the gait phases of individuals with SCI (p < 0.05). We also observed reliable imitation of ground truth after compensation. Overall, our research highlights the potential of wearable technology to improve the daily lives of individuals with disabilities through accurate and stable state assessment. |
28 | Dynamic Multi-Query Motion Planning with Differential Constraints and Moving Goals | Gentner, Michael, Technical University of Munich and BMW AG Zillenbiller, Fabian, Technical University of Munich and BMW AG Kraft, André, BMW AG, Germany Steinbach, Eckehard, Technical University of Munich | Keywords: Collision Avoidance, Motion and Path Planning, Industrial Robots | Abstract: Planning robot motions in complex environments is a fundamental research challenge and central to the autonomy, efficiency, and ultimately adoption of robots. While often the environment is assumed to be static, real-world settings, such as assembly lines, contain complex shaped, moving obstacles and changing target states. Therein robots must perform safe and efficient motions to achieve their tasks. In repetitive environments and multi-goal settings, reusable roadmaps can substantially reduce the overall query time. Most dynamic roadmap-based planners operate in state-time-space, which is computationally demanding. Interval-based methods store availabilities as node attributes and thereby circumvent the dimensionality increase. However, current approaches do not consider higher-order constraints, which can ultimately lead to collisions during execution. Furthermore, current approaches must replan when the goal changes. To this end, we propose a novel roadmap-based planner for systems with third-order differential constraints operating in dynamic environments with moving goals. We construct a roadmap with availabilities as node attributes. During the query phase, we use a Double-Integrator Minimum Time (DIMT) solver to recursively build feasible trajectories and accurately estimate arrival times. An exit node set in combination with a moving goal heuristic is used to efficiently find the fastest path through the roadmap to the moving goal. We evaluate our method with a simulated UAV operating in dynamic 2D environments and show that it also transfers to a 6-DoF manipulator. We show higher success rates than other state-of-the-art methods both in collision avoidance and reaching a moving goal. |
29 | Reactive and Safe Co-Navigation with Haptic Guidance | Coffey, Mela, Boston University Zhang, Dawei, Boston University Tron, Roberto, Boston University Pierson, Alyssa, Boston University | Keywords: Collision Avoidance, Telerobotics and Teleoperation, Human-Robot Collaboration | Abstract: We propose a co-navigation algorithm that enables a human and a robot to work together to navigate to a common goal. In this system, the human is responsible for making high-level steering decisions, and the robot, in turn, provides haptic feedback for collision avoidance and path suggestions while reacting to changes in the environment. Our algorithm uses optimized Rapidly-exploring Random Trees (RRT*) to generate paths to lead the user to the goal, via an attractive force feedback computed using a Control Lyapunov Function (CLF). We simultaneously ensure collision avoidance where necessary using a Control Barrier Function (CBF). We demonstrate our approach using simulations with a virtual pilot, and hardware experiments with a human pilot. Our results show that combining RRT* and CBFs is a promising tool for enabling collaborative human-robot navigation. |
30 | An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications | Leisiazar, Sahar, Simon Fraser University Park, Edward J., Simon Fraser University Lim, Angelica, Simon Fraser University Chen, Mo, Simon Fraser University | Keywords: Robot Companions, Collision Avoidance, AI-Enabled Robotics | Abstract: We propose a novel methodology for robotic follow-ahead applications that address the critical challenge of obstacle and occlusion avoidance. Our approach effectively navigates the robot while ensuring avoidance of collisions and occlusions caused by surrounding objects. To achieve this, we developed a high-level decision-making algorithm that generates short-term navigational goals for the mobile robot. Monte Carlo Tree Search is integrated with a Deep Reinforcement Learning method to enhance the performance of the decision-making process and generate more reliable navigational goals. Through extensive experimentation and analysis, we demonstrate the effectiveness and superiority of our proposed approach in comparison to the existing follow-ahead human-following robotic methods. Our code is available at https://github.com/saharLeisiazar/follow-ahead-ros. |
31 | Proactive Model Predictive Control with Multi-Modal Human Motion Prediction in Cluttered Dynamic Environments | Heuer, Lukas, Örebro University, Robert Bosch GmbH Palmieri, Luigi, Robert Bosch GmbH Rudenko, Andrey, Robert Bosch GmbH Mannucci, Anna, Robert Bosch GmbH Corporate Research Magnusson, Martin, Örebro University Arras, Kai Oliver, Bosch Research | Keywords: Collision Avoidance, Human-Aware Motion Planning, Motion and Path Planning | Abstract: For robots navigating in dynamic environments, exploiting and understanding uncertain human motion prediction is key to generate efficient, safe and legible actions. The robot may perform poorly and cause hindrances if it does not reason over possible, multi-modal future social interactions. With the goal of further enhancing autonomous navigation in cluttered environments, we propose a novel formulation for nonlinear model predictive control including multi-modal predictions of human motion. As a result, our approach leads to less conservative, smooth and intuitive human-aware navigation with reduced risk of collisions, and shows a good balance between task efficiency, collision avoidance and human comfort. To show its effectiveness, we compare our approach against the state of the art in crowded simulated environments, and with real-world human motion data from the THOR dataset. This comparison shows that we are able to improve task efficiency, keep a larger distance to humans and significantly reduce the collision time, when navigating in cluttered dynamic environments. Furthermore, the method is shown to work robustly with different state-of-the-art human motion predictors. |
32 | A Novel Obstacle-Avoidance Solution with Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators | Li, Weibing, Sun Yat-Sen University Yi, Zilian, Sun Yat-Sen University Zou, Yanying, Sun Yat-Sen University Wu, Haimei, Sun Yat-Sen University Yang, Yang, Sun Yat-Sen University Pan, Yongping, Sun Yat-Sen University | Keywords: Collision Avoidance, Optimization and Optimal Control, Redundant Robots | Abstract: Obstacle avoidance (OA) and joint-limit avoidance (JLA) are essential for redundant manipulators to ensure safe and reliable robotic operations. One solution to OA and JLA is to incorporate the involved constraints into a quadratic programming (QP), by solving which OA and JLA can be achieved. There exist a few non-iterative solvers such as zeroing neural networks (ZNNs), which can solve each sampled QP problem using only one iteration, yet no solution is suitable for OA and JLA due to the absence of some derivative information. To tackle these issues, this paper proposes a novel solution with a non-iterative neural controller termed NCP-ZNN for joint-constrained redundant manipulators. Unlike iterative methods, the neural controller involving derivative information proposed in this paper possesses some positive features including non-iterative computing and convergence with time. In this paper, the reestablished OA-JLA scheme is first introduced. Then, the design details of the neural controller are presented. After that, some comparative simulations based on a PA10 robot and an experiment based on a Franka Emika Panda robot are conducted, demonstrating that the proposed neural controller is more competent in OA and JLA. |
33 | TTC4MCP: Monocular Collision Prediction Based on Self-Supervised TTC Estimation | Li, Changlin, Shanghai Jiao Tong University Qian, Yeqiang, Shanghai Jiao Tong University Sun, Cong, Shanghai Jiao Tong University Yan, Weihao, Shanghai Jiao Tong University Wang, Chunxiang, Shanghai Jiaotong University Yang, Ming, Shanghai Jiao Tong University | Keywords: Collision Avoidance, Computer Vision for Transportation, Deep Learning for Visual Perception | Abstract: Vision-based collision prediction for autonomous driving is a challenging task due to the dynamic movement of vehicles and diverse types of obstacles. Most existing methods rely on object detection algorithms, which only predict predefined collision targets, such as vehicles and pedestrians, and cannot anticipate emergencies caused by unknown obstacles. To address this limitation, we propose a novel approach using pixel-wise time-to-collision (TTC) estimation for monocular collision prediction (TTC4MCP). Our approach predicts TTC and optical flow from monocular images and identifies potential collision areas using feature clustering and motion analysis. To overcome the challenge of training TTC estimation models without ground truth data in new scenes, we propose a self-supervised TTC training method, enabling collision prediction in a wider range of scenarios. TTC4MCP is evaluated on multiple road conditions and demonstrates promising results in terms of accuracy and robustness. |
34 | DAMON: Dynamic Amorphous Obstacle Navigation Using Topological Manifold Learning and Variational Autoencoding | Dastider, Apan, University of Central Florida Mingjie, Lin, University of Central Florida | Keywords: Collision Avoidance, Deep Learning Methods, Motion and Path Planning | Abstract: DAMON leverages manifold learning and vari- ational autoencoding to achieve obstacle avoidance, allowing for motion planning through adaptive graph traversal in a pre-learned low-dimensional hierarchically-structured manifold graph that captures intricate motion dynamics between a robotic arm and its obstacles. This versatile and reusable approach is applicable to various collaboration scenarios. The primary advantage of DAMON is its ability to embed information in a low-dimensional graph, eliminating the need for repeated computation required by current sampling-based methods. As a result, it offers faster and more efficient motion planning with significantly lower computational overhead and memory footprint. In summary, DAMON is a breakthrough methodology that addresses the challenge of dynamic obstacle avoidance in robotic systems and offers a promising solution for safe and efficient human-robot collaboration. Our approach has been experimentally validated on a 7-DoF robotic manipulator in both simulation and physical settings. DAMON enables the robot to learn and generate skills for avoiding previously-unseen obstacles while achieving predefined objectives. We also optimize DAMON’s design parameters and performance using an analytical framework. Our approach outperforms mainstream methodologies, including RRT, RRT*, Dynamic RRT*, L2RRT, and MpNet, with 40% more trajectory smoothness and over 65% improved latency performance, on average. |
35 | Gatekeeper: Online Safety Verification and Control for Nonlinear Systems in Dynamic Environments | Agrawal, Devansh, University of Michigan Chen, Ruichang, University of Michigan Panagou, Dimitra, University of Michigan, Ann Arbor | Keywords: Collision Avoidance, Motion and Path Planning | Abstract: This paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method to ensure that nonlinear systems can operate safely in dynamic environments despite limited perception. Gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step that ensures that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the environment. Our key contribution is that (A) we propose an algorithm to recursively construct committed trajectories, and (B) we prove that tracking the committed trajectory ensures the system is safe for all time into the future. The method is demonstrated on a complicated firefighting mission in a dynamic environment, and compares against the state-of-the-art techniques for similar problems. |
36 | Combinatorial Disjunctive Constraints for Obstacle Avoidance in Path Planning | Garcia, Raul, Rice University Hicks, Illya V., Rice University Huchette, Joey, Google Research | Keywords: Collision Avoidance, Motion and Path Planning, Optimization and Optimal Control | Abstract: We present a new approach for modeling avoidance constraints in 2D environments, in which waypoints are assigned to obstacle-free polyhedral regions. Constraints of this form are often formulated as mixed-integer programming (MIP) problems employing big-M techniques - however, these are generally not the strongest formulations possible with respect to the MIP's convex relaxation (so called ideal formulations), potentially resulting in larger computational burden. We instead model obstacle avoidance as combinatorial disjunctive constraints and leverage the independent branching scheme to construct small, ideal formulations. As our approach requires a biclique cover for an associated graph, we exploit the structure of this class of graphs to develop a fast subroutine for obtaining biclique covers in polynomial time. We also contribute an open-source Julia library named ClutteredEnvPathOpt to facilitate computational experiments of MIP formulations for obstacle avoidance. Experiments have shown our formulation is more compact and remains competitive on a number of instances compared with standard big-M techniques, for which solvers possess highly optimized procedures. |
37 | Reachability-Aware Collision Avoidance for Tractor-Trailer System with Non-Linear MPC and Control Barrier Function | Tang, Yucheng, University of Applied Sciences Karlsruhe Mamaev, Ilshat, Karlsruhe Institute of Technology Qin, Jing, Karlsruhe University of Applied Sciences Wurll, Christian, Karlsruhe University of Applied Sciences Hein, Björn, Karlsruhe University of Applied Sciences | Keywords: Collision Avoidance, Optimization and Optimal Control, Nonholonomic Motion Planning | Abstract: This paper proposes a reachability-aware model predictive control with a discrete control barrier function for backward obstacle avoidance for a tractor-trailer system. The framework incorporates the state-variant reachable set obtained through sampling-based reachability analysis and symbolic regression into the objective function of model predictive control. By optimizing the intersection of the reachable set and iterative non-safe region generated by the control barrier function, the system demonstrates better performance in terms of safety with a constant decay rate, while enhancing the feasibility of the optimization problem. The proposed algorithm improves real-time performance due to a shorter horizon and outperforms the state-of-the-art algorithms in the simulation environment and on a real robot. |
38 | Continuous Implicit SDF Based Any-Shape Robot Trajectory Optimization | Zhang, Tingrui, Zhejiang University Wang, Jingping, Zhejiang University Xu, Chao, Zhejiang University Gao, Alan, Fan'gang Gao, Fei, Zhejiang University | Keywords: Collision Avoidance, Whole-Body Motion Planning and Control, Motion and Path Planning | Abstract: Optimization-based trajectory generation methods are widely used in whole-body planning for robots. However, existing work either oversimplifies the robot’s geometry and environment representation, resulting in a conservative trajectory or suffers from a huge overhead in maintaining additional information such as the Signed Distance Field (SDF). To bridge the gap, we consider the robot as an implicit function, with its surface boundary represented by the zero-level set of its SDF. We further employ another implicit function to lazily compute the signed distance to the swept volume generated by the robot and its trajectory. The computation is efficient by exploiting continuity in space-time, and the implicit function guarantees precise and continuous collision evaluation even for nonconvex robots with complex surfaces. We also propose a trajectory optimization pipeline applicable to the implicit SDF. Simulation and real-world experiments validate the high performance of our approach for arbitrarily shaped robot trajectory optimization. The code will be released at https://github.com/ZJU-FAST-Lab/Implicit-SDF-Planner. |
39 | Robo-Centric ESDF: A Fast and Accurate Whole-Body Collision Evaluation Tool for Any-Shape Robotic Planning | Geng, Shuang, Zhejiang University Wang, Qianhao, Zhejiang University Xie, Lei, State Key Laboratory of Industrial Control Technology, Zhejiang Xu, Chao, Zhejiang University Cao, Yanjun, Zhejiang University, Huzhou Institute of Zhejiang University Gao, Fei, Zhejiang University | Keywords: Collision Avoidance, Motion and Path Planning | Abstract: For letting mobile robots travel flexibly through complicated environments, increasing attention has been paid to the whole-body collision evaluation. Most existing works either opt for the conservative corridor-based methods that impose strict requirements on the corridor generation, or ESDF-based methods that suffer from high computational overhead. It is still a great challenge to achieve fast and accurate whole-body collision evaluation. In this paper, we propose a Robo-centric ESDF (RC-ESDF) that is pre-built in the robot body frame and is capable of seamlessly applied to any-shape mobile robots, even for those with non-convex shapes. RC-ESDF enjoys lazy collision evaluation, which retains only the minimum information sufficient for whole-body safety constraint and significantly speed up trajectory optimization. Based on the analytical gradients provided by RC-ESDF, we optimize the position and rotation of robot jointly, with whole-body safety, smoothness, and dynamical feasibility taken into account. Extensive simulation and real-world experiments verified the reliability and generalizability of our method. |
40 | Global Map Assisted Multi-Agent Collision Avoidance Via Deep Reinforcement Learning Around Complex Obstacles | Du, Yuanyuan, Cuhk, Sz Zhang, Jianan, Peking University Xu, Jie, Cush, Sz Cheng, Xiang, Pku Cui, Shuguang, Cush, Sz | Keywords: Collision Avoidance, Motion and Path Planning, Reinforcement Learning | Abstract: State-of-the-art multi-agent collision avoidance algorithms face limitations when applied to cluttered public environments, where obstacles may have a variety of shapes and structures. The issue arises because most of these algorithms are agent-level methods. They concentrate solely on preventing collisions between the agents while the obstacles are handled merely out-of-policy. Obstacle-aware policies output an action considering both agents and obstacles. Current obstacle-aware algorithms, mainly based on Lidar sensor data, struggle to handle collision avoidance around complex obstacles. To resolve this issue, this paper investigates how to find a better way to travel around diverse obstacles. In particular, we present a global map assisted collision avoidance algorithm which, following the lead of a high-level goal guide and using an obstacle representation called distance map, considers other agents and obstacles simultaneously. Moreover, our model can be loaded into each agent individually, making it applicable to large maps or more agents. Simulation results indicate that our model outperforms the state-of-the-art algorithms, showing in scenarios with complex obstacles. We present a notion for incorporating global information in decentralized decision-making, along with a method for extending agent-level algorithms to adjust to cluttered environments in real-world scenarios. |
41 | A Geometric Sufficient Condition for Contact Wrench Feasibility | Li, Shenggao, University of Notre Dame Chen, Hua, Southern University of Science and Technology Zhang, Wei, Southern University of Science and Technology Wensing, Patrick M., University of Notre Dame | Keywords: Body Balancing, Humanoid and Bipedal Locomotion, Whole-Body Motion Planning and Control | Abstract: A fundamental problem in legged locomotion is to verify whether a desired trajectory satisfies all physical constraints, especially those,for maintaining the contacts. Although foot tipping can be avoided via the Zero Moment Point (ZMP) condition, preventing foot sliding and twisting leads to the more complex Contact Wrench Cone (CWC) constraints. This paper proposes an efficient algorithm to certify the inclusion of a net contact wrench in the CWC on flat ground with uniform friction. In addition to checking the ZMP criteria, the proposed method also verifies whether the linear force and the yaw moment are feasible. The key step in the algorithm is a new exact geometric characterization of the yaw moment limits in the case when the support polygon is approximated by a single supporting line. We propose two approaches to select this approximating line, providing an accurate inner approximation of the ground truth yaw moment limits with,only 18.80% (resp. 7.13%) error. The methods require only 1/150 (resp.,1/139) computation time compared to the exact CWC method based on conic,programming. As a benchmark, approximating the CWC using square friction pyramids requires similar computation times as the exact CWC, but has > 19.35% error. Unlike the ZMP condition, our method provides a,sufficient condition for contact wrench feasibility. |
42 | Aggregating Single-Wheeled Mobile Robots for Omnidirectional Movements | Wang, Meng, Beijing Institute for General Artificial Intelligence Su, Yao, Beijing Institute for General Artificial Intelligence Li, Hang, Beijing Institute for General Artificial Intelligence Li, Jiarui, Peking University Liang, Jixaing, Beihang University Liu, Hangxin, Beijing Institute for General Artificial Intelligence (BIGAI) | Keywords: Education Robotics, Art and Entertainment Robotics | Abstract: This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory-tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation setting. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs. |
43 | An On-Wall-Rotating Strategy for Effective Upstream Motion of Untethered Millirobot: Principle, Design and Demonstration (I) | Yang, Liu, City University of Hong Kong Zhang, Tieshan, City University of Hong Kong Huang, Han, City University of Hong Kong Ren, Hao, City University of Hongkong Shang, Wanfeng, Shenzhen Institutes of Advanced Technology, Chinese Academy of S Shen, Yajing, The Hong Kong University of Science and Technology | Keywords: on-wall-rotating, Medical Robots and Systems, Modeling, Control, and Learning for Soft Robots, Micro/Nano Robots | Abstract: Untethered miniature robots that can access narrow and harsh environments in the body show great potential for future biomedical applications. Despite many types of millirobot have been developed, swimming against the fast blood flow remains a big challenge due to the low staying still ability of the robot and the large hydraulic resistance from blood. This work proposes an on-wall-rotating strategy and a streamlined millirobot to achieve the effective upstream motion in the lumen. First, the principle of on-wall-rotating strategy and the dynamic motion model of the millirobot is established. Then, a critical safety angle θs is theoretically and experimentally analyzed for the safe and stable control of the robot. After that, a series of experiment are conducted to verify the proposed driving strategy. The resutls suggest that the robot is able to move at speed of 5 mm/s against flow velocity of 138 mm/s, which is comparable to the blood flow of 2700 mm3 /s and several times faster than other reported driving strategies. This work offers a new strategy for the untethered magnetic robot construction and control for blood vessels, which would promote the application of millirobot for biomedical engineering. |
44 | Smooth Stride Length Change of Rat Robot with a Compliant Actuated Spine Based on CPG Controller | Huang, Yuhong, Technische Universität München Bing, Zhenshan, Technical University of Munich Zhang, Zitao, Sun Yat-Sen University Huang, Kai, Sun Yat-Sen University Morin, Fabrice O., Technische Universität München Knoll, Alois, Tech. Univ. Muenchen TUM | Keywords: Robust/Adaptive Control, Motion Control, Biologically-Inspired Robots | Abstract: The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause significant and unexpected motion disturbances during stride length variations. To address this issue, this paper proposes a CPG model driven by spinal flexion and a novel oscillator that incorporates a circular limit cycle and accounts for the anticipated stride length transition process. This approach effectively matches the torque change with the dynamics of stride length changes, leading to lower energy consumption. Extensive simulations are conducted to evaluate the efficacy of the proposed oscillator and compare it with the original kinetic model and other CPG models. The results demonstrate that the designed CPG model with the proposed oscillator yields smoother gait transitions during stride length variations and reduces energy consumption. |
45 | Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals | Li, Tingguang, The Chinese University of Hong Kong Zhang, Yizheng, Tencent Zhang, Chong, Tencent Zhu, Qingxu, Tencent Sheng, Jiapeng, Shandong University Chi, Wanchao, Tencent Zhou, Cheng, Tencent Han, Lei, Tencent Robotics X | Keywords: Machine Learning for Robot Control, Reinforcement Learning, AI-Based Methods | Abstract: In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method consists of two steps: an imitation learning step to learn from motions of real animals, and a terrain adaptation step to enable generalization to unseen terrains. We capture motions from a Labrador on various terrains to facilitate terrain adaptive locomotion. Our experiments demonstrate that our policy can traverse various terrains and produce a natural-looking behavior. We deployed our method on the real quadruped robot Max via zero-shot simulation-to-reality transfer, achieving a speed of 1.1 m/s on stairs climbing. |
46 | A Stable Adaptive Extended Kalman Filter for Estimating Robot Manipulators Link Velocity and Acceleration | Baradaran Birjandi, Seyed Ali, Technical University of Munich Khurana, Harshit, EPFL Billard, Aude, EPFL Haddadin, Sami, Technical University of Munich | Keywords: Sensor Fusion, Kinematics | Abstract: One can estimate the velocity and acceleration of robot manipulators by utilizing nonlinear observers. This involves combining inertial measurement units (IMUs) with the motor encoders of the robot through a model-based sensor fusion technique. This approach is lightweight, versatile (suitable for a wide range of trajectories and applications), and straightforward to implement. In order to further improve the estimation accuracy while running the system, we propose to adapt the noise information in this paper. This would automatically reduce the system vulnerability to imperfect modelings and sensor changes. Moreover, viable strategies to maintain the system stability are introduced. Finally, we thoroughly evaluate the overall framework with a seven DoF robot manipulator whose links are equipped with IMUs. |
47 | Provably Correct Sensor-Driven Path-Following for Unicycles Using Monotonic Score Functions | Clark, Benton, University of Kentucky Hariprasad, Varun, Paul Laurence Dunbar High School Poonawala, Hasan A., University of Kentucky | Keywords: Sensor-based Control, Autonomous Vehicle Navigation, Machine Learning for Robot Control | Abstract: This paper develops a provably stable sensor-driven controller for path-following applications of robots with unicycle kinematics, one specific class of which is the wheeled mobile robot (WMR). The sensor measurement is converted to a scalar value (the score) through some mapping (the score function); the latter may be designed or learned. The score is then mapped to forward and angular velocities using a simple rule with three parameters. The key contribution is that the correctness of this controller only relies on the score function satisfying monotonicity conditions with respect to the underlying state - local path coordinates - instead of achieving specific values at all states. The monotonicity conditions may be checked online by moving the WMR, without state estimation, or offline using a generative model of measurements such as in a simulator. Our approach provides both the practicality of a purely measurement-based control and the correctness of state-based guarantees. We demonstrate the effectiveness of this path-following approach on both a simulated and a physical WMR that use a learned score function derived from a binary classifier trained on real depth images. |
48 | Contact Reduction with Bounded Stiffness for Robust Sim-To-Real Transfer of Robot Assembly | Nghia, Vuong, Nanyang Technological University Pham, Quang-Cuong, NTU Singapore | Keywords: Simulation and Animation, Reinforcement Learning, Machine Learning for Robot Control | Abstract: In sim-to-real Reinforcement Learning (RL), a policy is trained in a simulated environment and then deployed on the physical system. The main challenge of sim-to-real RL is to overcome the emph{reality gap} - the discrepancies between the real world and its simulated counterpart. Using generic geometric representations, such as convex decomposition, triangular mesh, signed distance field can improve simulation fidelity, and thus potentially narrow the reality gap. Common to these approaches is that many contact points are generated for geometrically-complex objects, which slows down simulation and may cause numerical instability. Contact reduction methods address these issues by limiting the number of contact points, but the validity of these methods for sim-to-real RL has not been confirmed. In this paper, we present a contact reduction method with bounded stiffness to improve the simulation accuracy. Our experiments show that the proposed method critically enables training RL policy for a tight-clearance double pin insertion task and successfully deploying the policy on a rigid, position-controlled physical robot. |
49 | Trajectory Tracking Via Multiscale Continuous Attractor Networks | Joseph, Therese, Queensland University of Technology Fischer, Tobias, Queensland University of Technology Milford, Michael J, Queensland University of Technology | Keywords: Neurorobotics, Cognitive Modeling | Abstract: Animals and insects showcase remarkably robust and adept navigational abilities, up to literally circumnavigating the globe. Primary progress in robotics inspired by these natural systems has occurred in two areas: highly theoretical computational neuroscience models, and handcrafted systems like RatSLAM and NeuroSLAM. In this research, we present work bridging the gap between the two, in the form of Multiscale Continuous Attractor Networks (MCAN), that combine the multiscale parallel spatial neural networks of the previous theoretical models with the real-world robustness of the robot-targeted systems, to enable trajectory tracking over large velocity ranges. To overcome the limitations of the reliance of previous systems on hand-tuned parameters, we present a genetic algorithm-based approach for automated tuning of these networks, substantially improving their usability. To provide challenging navigational scale ranges, we open source a flexible city-scale navigation simulator that adapts to any street network, enabling high throughput experimentation. In extensive experiments using the city-scale navigation environment and Kitti, we show that the system is capable of stable dead reckoning over a wide range of velocities and environmental scales, where a single-scale approach fails. |
50 | Design and Control of a Ballbot Drivetrain with High Agility, Minimal Footprint, and High Payload | Xiao, Chenzhang, University of Illinois at Urbana-Champaign Mansouri, Mahshid, University of Illinois at Urbana-Champaign Lam, David, University of Michigan - Ann Arbor Ramos, Joao, University of Illinois at Urbana-Champaign Hsiao-Wecksler, Elizabeth T., University of Illinois at Urbana-Champaign | Keywords: Body Balancing, Wheeled Robots, Underactuated Robots | Abstract: This paper presents the design and control of a ballbot drivetrain that aims to achieve high agility, minimal footprint, and high payload capacity while maintaining dynamic stability. Two hardware platforms and analytical models were developed to test design and control methodologies. The full-scale ballbot prototype (MiaPURE) was constructed using off-the-shelf components and designed to have agility, footprint, and balance similar to that of a walking human. The planar inverted pendulum testbed (PIPTB) was developed as a reduced-order testbed for quick validation of system performance. We then proposed a simple yet robust cascaded LQR-PI controller to balance and maneuver the ballbot drivetrain with a heavy payload. This is crucial because the drivetrain is often subject to high stiction due to elastomeric components in the torque transmission system. This controller was first tested in the PIPTB to compare with traditional LQR and cascaded PI-PD controllers, and then implemented in the ballbot drivetrain. The MiaPURE drivetrain was able to carry a payload of 60 kg, achieve a maximum speed of 2.3 m/s, and come to a stop from a speed of 1.4 m/s in 2 seconds in a selected translation direction. Finally, we demonstrated the omnidirectional movement of the ballbot drivetrain in an indoor environment as a payload-carrying robot and a human-riding mobility device. Our experiments demonstrated the feasibility of using the ballbot drivetrain as a universal mobility platform with agile movements, minimal footprint, and high payload capacity using our proposed design and control methodologies. |
51 | A Bayesian Reinforcement Learning Method for Periodic Robotic Control under Significant Uncertainty | Jia, Yuanyuan, Ritsumeikan University Uriguen Eljuri, Pedro Miguel, Ritsumeikan University Taniguchi, Tadahiro, Ritsumeikan University | Keywords: Dexterous Manipulation, Medical Robots and Systems, Reinforcement Learning | Abstract: This paper addresses the lack of research on periodic reinforcement learning for physical robot control by presenting a 3-phase periodic Bayesian reinforcement learning method for uncertain environments. Drawing on cognition theory, the proposed approach achieves effective convergence with fewer training episodes. The coach-based demonstration phase narrows the search space and establishes a foundation for a coarse-to-fine control strategy. The reconnaissance phase enhances adaptability by discovering a valuable global representation, and the operation phase produces accurate robotic control by applying the learned representation and periodically updating local information. Comparative analysis with state-of-the-art methods validates the efficacy of our approach on exemplar control tasks in simulation and a biomedical project involving a simulated cranial window task. |
52 | Residual Physics Learning and System Identification for Sim-To-Real Transfer of Policies on Buoyancy Assisted Legged Robots | Sontakke, Nitish Rajnish, Georgia Institute of Technology Chae, Hosik, University of California at Los Angeles Lee, Sangjoon, University of California, Los Angeles Huang, Tianle, Georgia Institute of Technology Hong, Dennis, UCLA Ha, Sehoon, Georgia Institute of Technology | Keywords: Model Learning for Control, Reinforcement Learning, Legged Robots | Abstract: The light and soft characteristics of Buoyancy Assisted Lightweight Legged Unit (BALLU) robots have a great potential to provide intrinsically safe interactions in environments involving humans, unlike many heavy and rigid robots. However, their unique and sensitive dynamics impose challenges to obtaining robust control policies in the real world. In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic). First, we model the nonlinear dynamics of the actuators by collecting hardware data and optimizing the simulation parameters. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy to match real-world trajectories, which enables us to model residual physics with greater fidelity. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones. We finally demonstrate that the improved simulator allows us to learn better walking and turning policies that can be successfully deployed on the hardware of BALLU. |
53 | DiffClothAI: Differentiable Cloth Simulation with Intersection-Free Frictional Contact and Differentiable Two-Way Coupling with Articulated Rigid Bodies | Yu, Xinyuan, National University of Singapore Zhao, Siheng, Nanjing University Luo, Siyuan, Xi'an Jiaotong University Yang, Gang, National University of Singapore Shao, Lin, National University of Singapore | Keywords: Simulation and Animation, Optimization and Optimal Control | Abstract: Differentiable Simulations have recently proven useful for various robotic manipulation tasks, including cloth manipulation. In robotic cloth simulation, it is crucial to maintain intersection-free properties. We present DiffClothAI, a differentiable cloth simulation with intersection-free friction contact and two-way coupling with articulated rigid bodies. DiffClothAI integrates the Project Dynamics and Incremental Potential Contact coherently and proposes an effective method to derive gradients in the Cloth Simulation. It also establishes the differentiable coupling mechanism between articulated rigid bodies and cloth. We conduct a comprehensive evaluation of DiffClothAI’s effectiveness and accuracy and perform a variety of experiments in downstream robotic manipulation tasks. Supplemental materials and videos are available on our project webpage. |
54 | Walk-Burrow-Tug: Legged Anchoring Analysis Using RFT-Based Granular Limit Surfaces | Huh, Tae Myung, UC Berkeley Cao, Cyndia, University of California Berkeley Aderibigbe, Jadesola, University of California, Berkeley Moon, Deaho, Korea Institute of Science and Technology Stuart, Hannah, UC Berkeley | Keywords: Contact Modeling, Legged Robots, Mobile Manipulation | Abstract: We develop a new resistive force theory based granular limit surface (RFT-GLS) method to predict and guide behaviors of forceful ground robots. As a case study, we harness a small mobile robotic system – MiniRQuad (296g) – to ‘walk-burrow-tug;’ it actively exploits ground anchoring by burrowing its legs to tug loads. RFT-GLS informs the selection of efficient strategies to transport sleds with varying masses. The granular limit surface (GLS), a wrench boundary that separates stationary and kinetic behavior, is computed using 3D resistive force theory (RFT) for a given body and set of motion twists. This limit surface is then used to predict the quasi-static trajectory of the robot when it fails to withstand an external load. We find that the RFT-GLS enables accurate force and motion predictions in laboratory tests. For control applications, a pre-composed state space map of the twist-wrench pairs enables computationally efficient simulations to improve robotic anchoring strategies. |
55 | Tube Mechanism with 3-Axis Rotary Joints Structure to Achieve Variable Stiffness Using Positive Pressure | Onda, Issei, Tohoku University Watanabe, Masahiro, Tohoku University Tadakuma, Kenjiro, Tohoku University Abe, Kazuki, Tohoku University Tadokoro, Satoshi, Tohoku University | Keywords: Mechanism Design, Hydraulic/Pneumatic Actuators, Flexible Robotics | Abstract: Studies on soft robotics have explored mechanisms for switching the stiffness of a robot structure. The hybrid soft-rigid approach, which combines soft materials and high-rigidity structures, is commonly used to achieve variable stiffness mechanisms. In particular, the positive-pressurization method has attracted significant attention in recent years as it can eliminate the constraints on driving pressure. Moreover, it can change the shape holding force according to internal pressure. In this study, a variable stiffness mechanism, comprising 3-axis rotary ball joints and a single chamber, was devised via frictional force using positive pressure. The prototype can change joint angles arbitrarily when no pressure is applied and can hold joint angles when positive pressure is applied. Using a theoretical model of the torque required to hold the joint angle, we simulated the holding torque using finite element modeling analysis and measured the holding torque in the pitch and roll directions when internal pressure was applied. Based on the interaction of the theoretical model, measurement, and FEM analysis, it was confirmed that the value of the holding torque in the roll direction was approximately π/2 times larger than that in the pitch direction for each value of the internal pressure. Further, we evaluated the FEM value, theoretical value, and measured value of the holding torque by performing pairwise numerical comparisons. Our approach will aid the design of effective stiffening mechanisms for soft robotics applications. |
56 | Timor Python: A Toolbox for Industrial Modular Robotics | Külz, Jonathan, Technical University of Munich Mayer, Matthias, Technical University of Munich Althoff, Matthias, Technische Universität München | Keywords: Cellular and Modular Robots, Methods and Tools for Robot System Design, Software Tools for Robot Programming | Abstract: Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators, they promise greater flexibility, improved maintainability, and cost-efficiency. However, there is no tool or standardized way to model and simulate assemblies of modules in the same way it has been done for robotic manipulators for decades. We introduce the Toolbox for Industrial Modular Robotics (Timor), a Python toolbox to bridge this gap and integrate modular robotics into existing simulation and optimization pipelines. Our open-source library offers model generation and task-based configuration optimization for MRRs. It can easily be integrated with existing simulation tools – not least by offering URDF export of arbitrary modular robot assemblies. Moreover, our experimental study demonstrates the effectiveness of Timor as a tool for designing modular robots optimized for specific use cases. |
57 | Ultra-Low Inertia 6-DOF Manipulator Arm for Touching the World | Nishii, Kazutoshi, Toyota Motor Corporation Okumatsu, Yohishiro, Toyota Motor Corporation Hatano, Akira, Toyota Motor Corporation | Keywords: Mechanism Design, Tendon/Wire Mechanism | Abstract: As robotic intelligence increases, so does the importance of agents that collect data from real-world environments. When learning in contact with the environment, one must consider how to minimize the impact on the environment and maintain reproducibility. To achieve this, the contact force with the environment must be reduced. One way to achieve this is to reduce the inertia of the arm. In this study, we present an arm we have developed with 6 degrees of freedom and low inertia. The inertia of our arm has been significantly reduced compared to previous research, and experiments have confirmed that it also has low joint friction torque and good contact sensitivity. |
58 | Determination of the Characteristics of Gears of Robot-Like Systems by Analytical Description of Their Structure | Landler, Stefan, Technical University of Munich Molina Blanco, Raúl, Technical University of Munich Otto, Michael, Technical University of Munich, Chair of Machine Elements, Gear Vogel-Heuser, Birgit, Technical University Munich Zimmermann, Markus, Technical University of Munich Stahl, Karsten, Technical University of Munich | Keywords: Methods and Tools for Robot System Design, Product Design, Development and Prototyping, Engineering for Robotic Systems | Abstract: The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far. |
59 | Tension Jamming for Deployable Structures | Hasegawa, Daniel, Harvard University Aktas, Buse, ETH Zurich Howe, Robert D., Harvard University | Keywords: Mechanism Design, Compliant Joints and Mechanisms, Soft Robot Materials and Design | Abstract: Deployable structures provide adaptability and versatility for applications such as temporary architectures, space structures, and biomedical devices. Jamming is a mechanical phenomenon with which dramatic changes in stiffness can be achieved by increasing the frictional and kinematic coupling between constituents in a structure by applying an external pressure. This study applies jamming, which has been primarily used in medium-scale soft robotics applications to large-scale deployable structures with components that are soft and compact during transport, but rigid upon deployment. It proposes a new jamming structure with a novel built-in actuation mechanism which enables high-performance at large scales: a composite beam made of rectangular segments along a cable which can be pre-tensioned and thus jammed. Two theoretical models are developed to provide insights into the mechanical behavior of the composite beams and predict their performance under loading. A scale model of a deployable bridge is built using the tension-based composite beams, and the bridge is deployed and assembled by air with a drone demonstrating the versatility and viability of the proposed approach for robotics applications. |
60 | Task2Morph: Differentiable Task-Inspired Framework for Contact-Aware Robot Design | Cai, Yishuai, National University of Defense Technology Yang, Shaowu, National University of Defense Technology Li, Minglong, National University of Defense Technology Chen, Xinglin, National University of Defense Technology Mao, Yunxin, National University of Defense Technology Yi, Xiaodong, National University of Defense Technology Yang, Wenjing, State Key Laboratory of High Performance Computing (HPCL), Schoo | Keywords: Evolutionary Robotics, AI-Enabled Robotics | Abstract: Optimizing the morphologies and the controllers that adapt to various tasks is a critical issue in the field of robot design, aka. embodied intelligence. Previous works typically model it as a joint optimization problem and use search-based methods to find the optimal solution in the morphology space. However, they ignore the implicit knowledge of task-to-morphology mapping which can directly inspire robot design. For example, flipping heavier boxes tends to require more muscular robot arms. This paper proposes a novel and general differentiable task-inspired framework for contact-aware robot design called Task2Morph. We abstract task features highly related to task performance and use them to build a task-to-morphology mapping. Further, we embed the mapping into a differentiable robot design process, where the gradient information is leveraged for both the mapping learning and the whole optimization. The experiments are conducted on three scenarios, and the results validate that Task2Morph outperforms DiffHand, which lacks a task-inspired morphology module, in terms of efficiency and effectiveness. |
61 | Constraint Programming for Component-Level Robot Design | Wilhelm, Andrew, Cornell University Napp, Nils, Cornell University | Keywords: Methods and Tools for Robot System Design, Formal Methods in Robotics and Automation, Product Design, Development and Prototyping | Abstract: Effective design automation for building robots would make development faster and easier while also less prone to design errors. However, complex multi-domain constraints make creating such tools difficult. One persistent challenge in achieving this goal of design automation is the fundamental problem of component selection, an optimization problem where, given a general robot model, components must be selected from a possibly large set of catalogs to minimize design objectives while meeting target specifications. Different approaches to this problem have used Monotone Co-Design Problems (MCDPs) or linear and quadratic programming, but these require judicious system approximations that affect the accuracy of the solution. We take an alternative approach formulating the component selection problem as a combinatorial optimization problem, which does not require any system approximations, and using constraint programming (CP) to solve this problem with a depth-first branch-and-bound algorithm. As the efficacy of CP critically depends upon the orderings of variables and their domain values, we present two heuristics specific to the problem of component selection that significantly improve solve time compared to traditional constraint satisfaction programming heuristics. We also add redundant constraints to the optimization problem to further improve run time by evaluating certain global constraints before all relevant variables are assigned. We demonstrate that our CP approach can find optimal solutions from over 20 trillion candidate solutions in only seconds, up to 48 times faster than an MCDP approach solving the same problem. Finally, for three different robot designs we build the corresponding robots to physically validate that the selected components meet the target design specifications. |
62 | Design and Implementation of a Two-Limbed 3T1R Haptic Device | Kang, Long, Nanjing University of Science and Technology Yang, Yang, Nanjing University of Information Science and Technology Yi, Byung-Ju, Hanyang University | Keywords: Mechanism Design, Haptics and Haptic Interfaces, Parallel Robots | Abstract: This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages. |
63 | Combining Measurement Uncertainties with the Probabilistic Robustness for Safety Evaluation of Robot Systems | Baek, Woo-Jeong, Karlsruhe Institute of Technology (KIT) Ledermann, Christoph, Karlsruhe Institute of Technology Asfour, Tamim, Karlsruhe Institute of Technology (KIT) Kroeger, Torsten, Karlsruher Institut Für Technologie (KIT) | Keywords: Methods and Tools for Robot System Design, Robot Safety, Probability and Statistical Methods | Abstract: In this paper, we present a method to engage measurement uncertainties with the probabilistic robustness to one system uncertainty measure. Providing a metric indicating the potential occurrence of dangerous situations is highly essential for safety-critical robot applications. Due to the difficulty of finding a quantifiable, unambiguous representation however, such a metric has not been derived to date. In case of sensory devices, measurement uncertainties are usually provided by manufacturer specifications. Apart from that, several contributions demonstrate that the accuracy of neural networks is verifiable via the robustness. However, state-of-the-art literature is mainly concerned with theoretical investigations such that scarce attention has been devoted to the transfer of the robustness to real-world applications. To fill this gap, we show how the probabilistic robustness can be made useful for evaluating quantitative safety limits. Our key idea is to exploit the analogy between measurement uncertainties and the probabilistic robustness: While measurement uncertainties reflect possible shifts due to technical limitations, the robustness refers to the tolerated amount of distortions in the input data for an unaltered output. Inspired by this analogy, we combine both measures to quantify the system uncertainty online. We validate our method in different settings under real-world conditions. Our findings exemplify that incorporating the novel uncertainty metric effectively prevents the rate of dangerous situations in Human-Robot Collaboration. |
64 | Computational Design of Closed-Chain Linkages: Respawn Algorithm for Generative Design | Ivolga, Dmitriy, ITMO University Nasonov, Kirill, ITMO University Borisov, Ivan, ITMO University Kolyubin, Sergey, ITMO University | Keywords: Mechanism Design, Legged Robots, Grippers and Other End-Effectors | Abstract: Designing robots is a multiphase process aimed at solving a multi-criteria optimization problem to find the best possible detailed design. Generative design (GD) aims to accelerate the design process compared to manual design, since GD allows exploring and exploiting the vast design space more efficiently. In the field of robotics, however, relevant research focuses mostly on the generation of fully-actuated open chain kinematics, which is trivial in mechanical engineering perspective. Within this paper, we address the problem of generative design of closed-chain linkage mechanisms. A GD algorithm has to be able to generate meaningful mechanisms which satisfy conditions of existence. We propose an optimization-driven algorithm for generation of planar closed-chain linkages to follow a predefined trajectory. The algorithm creates an unlimited range of physically reproducible design alternatives that can be further tested in simulation. These tests could be done in order to find solutions that satisfy extra criteria, e.g., desired dynamic behavior or low energy consumption. The proposed algorithm is called "respawn" since it builds a new linkage after the ancestor has been tested in a virtual environment in pursuit for the optimal solution. To show that the algorithm is general enough, we show a set of generated linkages that can be used for a wide class of robots. |
65 | On Designing a Learning Robot: Improving Morphology for Enhanced Task Performance and Learning | Sorokin, Maks, Georgia Institute of Technology Fu, Chuyuan, X, the Moonshot Factory Tan, Jie, Google Liu, Karen, Stanford University Bai, Yunfei, Google X Lu, Wenlong, Everyday Robots, X the Moonshot Factory Ha, Sehoon, Georgia Institute of Technology Khansari, Mohi, Google X | Keywords: Mechanism Design, Visual Learning, Evolutionary Robotics | Abstract: As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design practices overlook the impact of perception and design choices on a robot's learning capabilities. To address this gap, we propose a comprehensive methodology that accounts for the interplay between the robot's perception, hardware characteristics, and task requirements. Our approach optimizes the robot's morphology holistically, leading to improved learning and task execution proficiency. To achieve this, we introduce a Morphology-AGnostIc Controller (MAGIC), which helps with the rapid assessment of different robot designs. The MAGIC policy is efficiently trained through a novel PRIvileged Single-stage learning via latent alignMent (PRISM) framework, which also encourages behaviors that are typical of robot onboard observation. Our simulation-based results demonstrate that morphologies optimized holistically improve the robot performance by 15-20% on various manipulation tasks, and require 25x less data to match human-expert made morphology performance. In summary, our work contributes to the growing trend of learning-based approaches in robotics and emphasizes the potential in designing robots that facilitate better learning. |
66 | Development of a Dynamic Quadruped with Tunable, Compliant Legs | Chen, Fuchen, Arizona State University Tao, Weijia, Arizona State University Aukes, Daniel, Arizona State University | Keywords: Mechanism Design, Compliant Joints and Mechanisms, Legged Robots | Abstract: To facilitate the study of how passive leg stiffness influences locomotion dynamics and performance, we have developed an affordable and accessible 400 g quadruped robot driven by tunable compliant laminate legs, whose series and parallel stiffness can be easily adjusted; fabrication only takes 2.5 hours for all four legs. The robot can trot at 0.52 m/s or 4.4 body lengths per second with a 3.2 cost of transport (COT). Through locomotion experiments in both the real world and simulation we demonstrate that legs with different stiffness have an obvious impact on the robot’s average speed, COT, and pronking height. When the robot is trotting at 4 Hz in the real world, changing the leg stiffness yields a maximum improvement of 37.1% in speed and 62.0% in COT, showing its great potential for future research on locomotion controller designs and leg stiffness optimizations. |
67 | A Passive Compliance Obstacle Crossing Robot for Power Line Inspection and Maintenance | Chen, Minghao, Institute of Automation, Chinese Academy of Sciences Cao, Yinghua, Institute of Automation，Chinese Academy of Sciences Tian, Yunong, Institute of Automation, Chinese Academy of Sciences Li, En, Institute of Automation, Chinese Academy of Sciences Liang, Zize, Institute of Automation, Chinese Academy of Sciences Tan, Min, Institute of Automation, Chinese Academy of Sciences | Keywords: Mechanism Design, Industrial Robots, Engineering for Robotic Systems | Abstract: In scenarios of the overhead power line system, manual methods are inefficient and unsafe. Meanwhile, the majority of cantilevered robots have poor efficiency when crossing obstacles. This paper proposes a novel power line inspection and maintenance robot to solve these problems. The robot employs a passive compliance obstacle-crossing principle, which could rapidly cross obstacles with the cooperation of gas springs and climbing wheels. Under high payload, the robot could take 5-15 seconds without any complex strategies to roll over obstacles. A variable configuration platform is also designed, which has a multiple line mode and a single line mode. It makes the robot suitable for different kinds of overhead power lines. Meanwhile, the related adaptability analyses are presented. Manipulators are also installed to help the robot perform specific maintenance tasks. The results of lab experiments and field tests reveal that the robot could stably and rapidly cross obstacles, such as suspension clamps, vibration dampers, and spacers, and could perform three kinds of maintenance tasks on the line. |
68 | Open Robot Hardware: Progress, Benefits, Challenges, and Best Practices (I) | Patel, Vatsal, Yale University Liarokapis, Minas, The University of Auckland Dollar, Aaron, Yale University | Keywords: Methods and Tools for Robot System Design, Product Design, Development and Prototyping, Mechanism Design | Abstract: Open-source projects have seen widespread adoption and improved availability in robotics over recent years. The rapid pace of progress in robotics is in part fueled by open-source projects, allowing researchers to implement novel ideas and approaches quickly. Open-source hardware in particular lowers the barrier of entry to new technologies, and can further accelerate innovation in robotics. But it is also more difficult to propagate in comparison to software because it requires replicating physical components. We present a review on Open Robot Hardware (ORH), by first highlighting key benefits and challenges encountered by users and developers of ORH, and relaying some best practices that can be adopted in developing an ORH. Then, we survey over 60 major ORH works in the different domains within robotics. Lastly, we identify strategies exemplified by the surveyed works to further detail the development process and guide developers through the design, documentation, and dissemination stages of an ORH project. |
69 | Modelling of Tendon Driven Robot Based on Constraint Analysis and Pseudo-Rigid Body Model | Troeung, Charles, Monash University Liu, Shaotong, Monash University Chen, Chao, Monash University | Keywords: Modeling, Control, and Learning for Soft Robots, Tendon/Wire Mechanism, Soft Robot Applications | Abstract: Quasi-static models of tendon-driven continuum robots (TDCR) require consideration of both the kinematic and static conditions simultaneously. While the Pseudo-Rigid Body (PRB-3R) model has been demonstrated to be efficient, existing works ignore the mechanical effect of the tendons such as elongation. In addition, the static equilibrium equations for the partially constrained tendons have been expressed in different forms within the literature. This leads to inconsistent simulation results which have not been validated by experimental data when external loads are applied. Furthermore, the inverse problem for solving the required inputs for a prescribed end effector pose has not been studied for the PRB-3R model. In this work, we introduce a new modelling approach based on constraint analysis (CA) of a multi-body system and Lagrange multipliers to systematically derive all the relevant governing equations required for a planar TDCR. This method can include tendon mechanics and efficiently solve for the direct and inverse kinetostatic models with either forces or displacements as the actuation inputs. We validate the proposed CA method using numerical simulation of a benchmark model and experimental data. |
70 | An Improved Koopman-MPC Framework for Data-Driven Modeling and Control of Soft Actuators | Wang, Jiajin, Southeast University Xu, Baoguo, Southeast University Lai, Jianwei, Southeast University Wang, Yifei, Southeast University Hu, Cong, Guilin University of Electronic Technology Li, Huijun, Southeast University Song, Aiguo, Southeast University | Keywords: Modeling, Control, and Learning for Soft Robots, Soft Sensors and Actuators | Abstract: The challenge of achieving precise control of soft actuators with strong nonlinearity is mainly due to the difficulty of deriving models suitable for model-based control techniques. Fortunately, Koopman operator provides a data-driven method for constructing control-oriented models of nonlinear systems to achieve model predictive control (MPC). It is called the Koopman-MPC framework, which is theoretically effective for soft actuators. Nevertheless, in this framework, a critical challenge is to select correct basis functions for Koopman-based modeling. Furthermore, there is room for improvement in control performance. To overcome these problems, this letter presents an improved Koopman-MPC framework to efficiently implement model-based control techniques for soft actuators. Firstly, we propose a systematic method for selecting the basis functions, which extends the measurement coordinates with derivative and time-delay coordinates and uses the spares identification of nonlinear dynamics (SINDy) algorithm. Secondly, an incremental model predictive control with dynamic constraints (IMPCDC) is developed based on the Koopman model. Finally, several comparative experiments are conducted to verify the utility of the improved Koopman-MPC framework for data-driven modeling and control of soft actuators. |
71 | Soft Robot Shape Estimation: A Load-Agnostic Geometric Method | Sorensen, Christian, Brigham Young University Killpack, Marc, Brigham Young University | Keywords: Modeling, Control, and Learning for Soft Robots, Soft Sensors and Actuators, Soft Robot Applications | Abstract: In this paper we present a novel kinematic representation of a soft continuum robot to enable full shape estimation using a purely geometric solution. The kinematic representation involves using length varying piecewise constant curvature segments to describe the deformed shape of the robot. Based on this kinematic representation, we can use overlapping length sensors to estimate the shape of continuously deformable bodies without prior knowledge of the current loading conditions. We show an implementation that assumes one change in curvature along the length of a joint, using string potentiometers as an arc length sensor, and an orientation measurement from the tip of the continuum joint. For 56 randomized joint configurations, we estimate the shape of a 250 mm long continually deformable robot with less then 2.5 mm of average error. The average error is reported for each of the 10 different equally spaced points along the length, demonstrating the ability to accurately represent the full shape of the soft robot. |
72 | Robust Generalized Proportional Integral Control for Trajectory Tracking of Soft Actuators in a Pediatric Wearable Assistive Device | Mucchiani, Caio, University of California Riverside Liu, Zhichao, University of California, Riverside Sahin, Ipsita, University of California, Riverside Kokkoni, Elena, University of California, Riverside Karydis, Konstantinos, University of California, Riverside | Keywords: Modeling, Control, and Learning for Soft Robots, Soft Robot Applications, Wearable Robotics | Abstract: Soft robotics hold promise in the development of safe yet powered assistive wearable devices for infants. Key to this is the development of closed-loop controllers that can help regulate pneumatic pressure in the device's actuators in an effort to induce controlled motion at the user's limbs and be able to track different types of trajectories. This work develops a controller for soft pneumatic actuators aimed to power a pediatric soft wearable robotic device prototype for upper extremity motion assistance. The controller tracks desired trajectories for a system of soft pneumatic actuators supporting two-degree-of-freedom shoulder joint motion on an infant-sized engineered mannequin. The degrees of freedom assisted by the actuators are equivalent to shoulder motion (abduction/adduction and flexion/extension). Embedded inertial measurement unit sensors provide real-time joint feedback. Experimental data from performing reaching tasks using the engineered mannequin are obtained and compared against ground truth to evaluate the performance of the developed controller. Results reveal the proposed controller leads to accurate trajectory tracking performance across a variety of shoulder joint motions. |
73 | Data-Efficient Online Learning of Ball Placement in Robot Table Tennis | Tobuschat, Philip, Max Planck Institue for Intelligent Systems, Tübingen Ma, Hao, Max Planck Institute for Intelligent Systems Büchler, Dieter, Max Planck Institute for Intelligent Systems Tübingen Schölkopf, Bernhard, Max Planck Institute for Intelligent Systems Muehlebach, Michael, ETH | Keywords: Modeling, Control, and Learning for Soft Robots, Bioinspired Robot Learning, Machine Learning for Robot Control | Abstract: We present an implementation of an online optimization algorithm for hitting a predefined target when returning ping-pong balls with a table tennis robot. The online algorithm optimizes over so-called interception policies, which define the manner in which the robot arm intercepts the ball. In our case, these are composed of the state of the robot arm (position and velocity) at interception time. Gradient information is provided to the optimization algorithm via the mapping from the interception policy to the landing point of the ball on the table, which is approximated with a black-box and a grey-box approach. Our algorithm is applied to a robotic arm with four degrees of freedom that is driven by pneumatic artificial muscles. As a result, the robot arm is able to return the ball onto any predefined target on the table after about 2-5 iterations. We highlight the robustness of our approach by showing rapid convergence with both the black-box and the grey-box gradients. In addition, the small number of iterations required to reach close proximity to the target also underlines the sample efficiency. A demonstration video can be found here: https://youtu.be/VC3KJoCss0k. |
74 | Learning Reduced-Order Soft Robot Controller | Liang, Chen, Zhejiang University Gao, Xifeng, Tencent America Wu, Kui, Tencent Pan, Zherong, Tencent America | Keywords: Modeling, Control, and Learning for Soft Robots, Soft Robot Applications, Optimization and Optimal Control | Abstract: Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while learning-based controller optimization methods are sensitive to hyper-parameter tuning. To overcome these limitations, we hypothesize that high fidelity soft robots can be both simulated and controlled by restricting to low-dimensional spaces. Under such assumption, we propose a two-stage algorithm to identify such simulation- and control-spaces. Our method first identifies the so-called simulation-space that captures the salient deformation modes, to which the robot's governing equation is restricted. We then identify the control-space, to which control signals are restricted. We propose a multi-fidelity Riemannian Bayesian bilevel optimization to identify task-specific control spaces. We show that the dimension of control-space can be less than 10 for a high-DOF soft robot to accomplish walking and swimming tasks, allowing low-dimensional MPC controllers to be applied to soft robots with tractable computational complexity. |
75 | A Single-Parameter Model for Soft Bellows Actuators under Axial Deformation and Loading | Treadway, Emma, Trinity University Brei, Melissa, University of Michigan Sedal, Audrey, McGill University Gillespie, Brent, University of Michigan | Keywords: Modeling, Control, and Learning for Soft Robots, Soft Sensors and Actuators, Hydraulic/Pneumatic Actuators | Abstract: Soft fluidic actuators are becoming popular for their backdrivability, potential for high power density, and their support for power supply through flexible tubes. Control and design of such actuators requires serviceable models that describe how they relate fluid pressure and flow to mechanical force and motion. We present a simple 2-port model of a bellows actuator that accounts for the relationships among fluid and mechanical variables imposed by the kinematics of the deforming bellows structure and accounts for elastic energy stored in the actuator’s thermoplastic material structure. Elastic energy storage due to axial deformation is captured by revolving a differential strip whose linear elastic behavior is a nonlinear function of the actuator length. The model is evaluated through experiments in which either actuator length and pressure or force and pressure are imposed. The model has an error of 9.8% of the force range explored and yields insight into the effects of geometry changes. The resulting model can be used for model-based control or actuator design across the full operating range and can be exercised under either imposed force or imposed actuator length. |
76 | Task and Configuration Space Compliance of Continuum Robots Via Lie Group and Modal Shape Formulations | Orekhov, Andrew, Carnegie Mellon University Johnston, Garrison, Vanderbilt University Simaan, Nabil, Vanderbilt University | Keywords: Modeling, Control, and Learning for Soft Robots, Kinematics, Flexible Robotics | Abstract: Continuum robots suffer large deflections due to internal and external forces. Accurate modeling of their passive compliance is necessary for accurate environmental interaction, especially in scenarios where direct force sensing is not practical. This paper focuses on deriving analytic formulations for the compliance of continuum robots that can be modeled as Kirchhoff rods. Compared to prior works, the approach presented herein is not subject to the constant-curvature assumptions to derive the configuration space compliance, and we do not rely on computationally-expensive finite difference approximations to obtain the task space compliance. Using modal approximations over curvature space and Lie group integration, we obtain closed-form expressions for the task and configuration space compliance matrices of continuum robots, thereby bridging the gap between constant-curvature analytic formulations of configuration space compliance and variable curvature task space compliance. We first present an analytic expression for the compliance of a single Kirchhoff rod. We then extend this formulation for computing both the task space and configuration space compliance of a tendon-actuated continuum robot. We then use our formulation to study the tradeoffs between computation cost and modeling accuracy as well as the loss in accuracy from neglecting the Jacobian derivative term in the compliance model. Finally, we experimentally validate the model on a tendon-actuated continuum segment, demonstrating the model's ability to predict passive deflections with error below 11.5% percent of total arc length. |
77 | A Localization Framework for Boundary Constrained Soft Robots | Tanaka, Koki, Illinois Institute of Technology Zhou, Qiyuan, Illinois Institute of Technology Srivastava, Ankit, Illinois Institute of Technology Spenko, Matthew, Illinois Institute of Technology | Keywords: Modeling, Control, and Learning for Soft Robots, Localization, Soft Robot Applications | Abstract: Soft robots possess unique capabilities for adapting to the environment and interacting with it safely. However, their deformable nature also poses challenges for controlling their movement. In particular, the large deformations of a soft robot make it difficult to localize its individual body parts, which in turn impedes effective control. This paper introduces a novel localization framework designed for soft robots that are constrained by boundaries and benefit from unique hardware architecture. To this end, we propose a method that exploits the flexible boundaries of the robot to create an onboard sensor capable of measuring the relative distances between its sub-robots. This measurement data is incorporated into a linear Kalman filter for accurate localization. We evaluate the framework's performance in benchmark and dynamic cases and demonstrate its effectiveness in improving localization accuracy compared to an IMU-based approach. The results also show that the proposed method achieves sufficient localization accuracy for contact-based mapping, enabling the robot to sense the location of obstacles in the environment. Finally, we validate the proposed framework using a physical prototype of a boundary-constrained soft robot and demonstrate its ability to accurately estimate the robot's shape. This framework has the potential to enable soft robots to autonomously navigate and map unknown environments, which could be beneficial for a variety of exploration tasks. |
78 | EViper: A Scalable Platform for Untethered Modular Soft Robots | Cheng, Hsin, Princeton University Zheng, Zhiwu, Princeton University Kumar, Prakhar, Princeton University Afridi, Wali, Ithaca Senior High School Kim, Ben, Princeton University Wagner, Sigurd, Princeton University Verma, Naveen, Princeton University Sturm, James, Princeton University Chen, Minjie, Princeton University | Keywords: Modeling, Control, and Learning for Soft Robots | Abstract: Soft robots present unique capabilities, but have been limited by the lack of scalable technologies for construction and the complexity of algorithms for efficient control and motion. These depend on soft-body dynamics, high-dimensional actuation patterns, and external/onboard forces. This paper presents scalable methods and platforms to study the impact of weight distribution and actuation patterns on fully untethered modular soft robots. An extendable Vibrating Intelligent Piezo-Electric Robot (eViper), together with an open-source Simulation Framework for Electroactive Robotic Sheet (SFERS) implemented in PyBullet, was developed as a platform to analyze the complex weight-locomotion interaction. By integrating power electronics, sensors, actuators, and batteries onboard, the eViper platform enables rapid design iteration and evaluation of different weight distribution and control strategies for the actuator arrays. The design supports both physics-based modeling and data-driven modeling via onboard automatic data-acquisition capabilities. We show that SFERS can provide useful guidelines for optimizing the weight distribution and actuation patterns of the eViper, thereby achieving maximum speed or minimum cost of transport (COT). |
79 | Domain Randomization for Robust, Affordable and Effective Closed-Loop Control of Soft Robots | Tiboni, Gabriele, Politecnico Di Torino Protopapa, Andrea, Politecnico Di Torino Tommasi, Tatiana, Politecnico Di Torino Averta, Giuseppe, Politecnico Di Torino | Keywords: Modeling, Control, and Learning for Soft Robots, Reinforcement Learning | Abstract: Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modeling a daunting task, and in many cases only an approximated description is available. This challenge makes reinforcement learning (RL) based approaches inefficient when deployed on a realistic scenario, due to the large domain gap between models and the real platform. In this work, we demonstrate, for the first time, how Domain Randomization (DR) can solve this problem by enhancing RL policies for soft robots with: i) robustness w.r.t. unknown dynamics parameters; ii) reduced training times by exploiting drastically simpler dynamic models for learning; iii) better environment exploration, which can lead to exploitation of environmental constraints for optimal performance. Moreover, we introduce a novel algorithmic extension of previous adaptive domain randomization methods for the automatic inference of dynamics parameters for deformable objects. We provide an extensive evaluation in simulation on four different tasks and two soft robot designs, opening interesting perspectives for future research on Reinforcement Learning for closed-loop soft robot control. |
80 | Implementation of a Cosserat Rod-Based Configuration Tracking Controller on a Multi-Segment Soft Robotic Arm | Doroudchi, Azadeh, Arizona State University Qiao, Zhi, ASU Zhang, Wenlong, Arizona State University Berman, Spring, Arizona State University | Keywords: Modeling, Control, and Learning for Soft Robots, Motion Control, Distributed Robot Systems | Abstract: Controlling soft continuum robotic arms is challenging due to their hyper-redundancy and dexterity. In this paper we experimentally demonstrate, for the first time, closed-loop control of the configuration space variables of a soft robotic arm, composed of independently controllable segments, using a Cosserat rod model of the robot and the distributed sensing and actuation capabilities of the segments. Our controller solves the inverse dynamic problem by simulating the Cosserat rod model in MATLAB using a computationally efficient numerical solution scheme, and it applies the computed control output to the actual robot in real time. The position and orientation of the tip of each segment are measured in real time, while the remaining unknown variables that are needed to solve the inverse dynamics are estimated simultaneously in the simulation. We implement the controller on a multi-segment silicone robotic arm with pneumatic actuation, using a motion capture system to measure the segments' positions and orientations. The controller is used to reshape the arm into configurations that are achieved through combinations of bending and extension deformations in 3D space. Although the possible deformations are limited for this robot platform, our study demonstrates the potential for implementing the control approach on a wide range of continuum robots in practice. The resulting tracking performance indicates the effectiveness of the controller and the accuracy of the simulated Cosserat rod model. |
81 | Closed Loop Static Control of Multi-Magnet Soft Continuum Robots | Pittiglio, Giovanni, Harvard University Orekhov, Andrew, Carnegie Mellon University da Veiga, Tomas, University of Leeds Calò, Simone, University of Leeds Chandler, James Henry, University of Leeds Simaan, Nabil, Vanderbilt University Valdastri, Pietro, University of Leeds | Keywords: Force Control, Medical Robots and Systems, Formal Methods in Robotics and Automation | Abstract: This paper discusses a novel static control approach applied to magnetic soft continuum robots (MSCRs). Our aim is to demonstrate the control of a multi-magnet soft continuum robot (SCR) in 3D. The proposed controller, based on a simplified yet accurate model of the robot, has a high update rate and is capable of real-time shape control. For the actuation of the MSCR, we employ the dual external permanent magnet (dEPM) platform and we sense the shape via fiber Bragg grating (FBG). The employed actuation system and sensing technique makes the proposed approach directly applicable to the medical context. We demonstrate that the proposed controller, running at approximately 300 Hz, is capable of shape tracking with a mean error of 8.5% and maximum error of 35.2% .We experimentally show that the static controller is 25.9% more accurate than a standard PID controller in shape tracking |
82 | IF-Based Trajectory Planning and Cooperative Control for Transportation System of Cable Suspended Payload with Multi UAVs | Zhang, Yu, Northeastern University, China Xu, Jie, Northeastern University, China Zhao, Cheng, Northeastern University, China Dong, Jiuxiang, Northeastern University, China | Keywords: Distributed Robot Systems, Multi-Robot Systems, Path Planning for Multiple Mobile Robots or Agents | Abstract: In this paper, we tackle the control and trajectory planning problems for the cooperative transportation system of cable-suspended payload with multi Unmanned Aerial Vehicles (UAVs). Firstly, a payload controller is presented considering the dynamic coupling between the UAV and the payload to accomplish the active suppression of payload swing and the complex payload trajectory tracking. Secondly, different from the simplification of obstacles in most approaches, we propose three Insetting Formation (IF) algorithms for the complete obstacle shape to generate collision-free waypoints for the cooperative transportation system. An IF strategy is proposed by integrating three IF algorithms to improve the success rate of obstacle avoidance and reduce the algorithm complexity for performing the aggressive flight. Finally, we verify the robustness and high performance of the proposed algorithm through benchmark comparison and real-world experiments. Moreover, our source code is released as an open-source ros package. |
83 | Cooperative Dual-Arm Control for Heavy Object Manipulation Based on Hierarchical Quadratic Programming | Dio, Maximilian, Friedrich-Alexander-Universität Erlangen-Nürnberg Völz, Andreas, Friedrich-Alexander-Universität Erlangen-Nürnberg Graichen, Knut, Friedrich Alexander University Erlangen-Nürnberg | Keywords: Cooperating Robots, Dual Arm Manipulation, Optimization and Optimal Control | Abstract: This paper presents a new control scheme for cooperative dual-arm robots manipulating heavy objects. The proposed method uses the full dynamical model of the kinematically coupled robot system and builds on a gls{hqp} formulation to enforce dynamical inequality constraints such as joint torques or internal loads. This ensures optimal tracking of an object trajectory, while additional objectives with lower priority are optimized on the prior solution space. Therefore, the redundancy of the inherent load distribution problem between the two arms can be eliminated. With this approach, higher object loads can be manipulated compared to non-optimized methods. Simulations with a 14~gls{dof} dual-arm robotic system demonstrate the effectiveness of the proposed control method. The real-time feasibility is guaranteed with an average computation time of less than 0.35 milliseconds at a control rate of 1 kilohertz. |
84 | Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning | Westheider, Jonas, University Bonn Rückin, Julius, University of Bonn Popovic, Marija, University of Bonn | Keywords: Path Planning for Multiple Mobile Robots or Agents, Reinforcement Learning, Cooperating Robots | Abstract: Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers improved spatial coverage and robustness against individual failures. However, a key challenge is cooperative path planning for the UAVs to efficiently achieve a joint mission goal. We propose a novel multi-agent informative path planning approach based on deep reinforcement learning for adaptive terrain monitoring scenarios using UAV teams. We introduce new network feature representations to effectively learn path planning in a 3D workspace. By leveraging a counterfactual baseline, our approach explicitly addresses credit assignment to learn cooperative behaviour. Our experimental evaluation shows improved planning performance, i.e. maps regions of interest more quickly, with respect to non-counterfactual variants. Results on synthetic and real-world data show that our approach has superior performance compared to state-of-the-art non-learning-based methods, while being transferable to varying team sizes and communication constraints. |
85 | Collective Intelligence for 2D Push Manipulations with Mobile Robots | Kuroki, So, The University of Tokyo Matsushima, Tatsuya, The University of Tokyo Jumpei, Arima, Matsuo Institute Furuta, Hiroki, The University of Tokyo Matsuo, Yutaka, The University of Tokyo Gu, Shixiang Shane, OpenAI Tang, Yujin, Google | Keywords: Cooperating Robots, Mobile Manipulation, Imitation Learning | Abstract: While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning difficulties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a differentiable soft-body physics simulator into an attentionbased neural network, our multi-robot push manipulation system achieves better performance than baselines. In addition, our system also generalizes to configurations not seen during training and is able to adapt toward task completions when external turbulence and environmental changes are applied. |
86 | Emergent Cooperative Behavior in Distributed Target Tracking with Unknown Occlusions | Li, Tianqi, Texas A&M University Krakow, Lucas, Texas A&M University Gopalswamy, Swaminathan, Texas A&M University | Keywords: Cooperating Robots, Reactive and Sensor-Based Planning, Behavior-Based Systems | Abstract: Tracking multiple moving objects of interest (OOI) with multiple robot systems (MRS) has been addressed by active sensing that maintains a shared belief of OOIs and plans the motion of robots to maximize the information quality. Mobility of robots enables the behavior of pursuing better visibility, which is constrained by sensor field of view (FoV) and occlusion objects. We first extend prior work to detect, maintain and share occlusion information explicitly, allowing us to generate occlusion-aware planning even if a priori semantic occlusion information is unavailable. The efficacy of active sensing approaches is often evaluated according to estimation error and information gain metrics. However, these metrics do not directly explain the level of cooperative behavior engendered by the active sensing algorithms. Next, we extract different emergent cooperative behaviors that stem from the same underlying algorithms but manifest differently under differing scenarios. In particular, we highlight and demonstrate three emergent behavior patterns in active sensing MRS: (i) Change of tracking responsibility between agents when tracking trajectories with divergent directions or due to a re-allocation of the resource among heterogeneous agents; (ii) Awareness of occlusions to a trajectory and temporal leave-and-return of the sensing agent; (iii) Sharing of local occlusion objects in MRS that subsequently improves the awareness of occlusion. |
87 | Multi-Objective Sparse Sensing with Ergodic Optimization | Rao, Ananya, Carnegie Mellon University Choset, Howie, Carnegie Mellon University | Keywords: Motion and Path Planning, Path Planning for Multiple Mobile Robots or Agents, Multi-Robot Systems | Abstract: We consider a search problem where a robot has one or more types of sensors, each suited to detecting different types of targets or target information. Often, information in the form of a distribution of possible target locations, or locations of interest, may be available to guide the search. When multiple types of information exist, then a distribution for each type of information must also exist, thereby making the search problem that uses these distributions to guide the search a multi-objective one. In this paper, we consider a multi-objective search problem when the ”cost” to use a sensor is limited. To this end, we leverage the ergodic metric, which drives agents to spend time in regions proportional to the expected amount of information there. We define the multi-objective sparse sensing ergodic (MO-SS-E) metric in order to optimize when and where each sensor measurement should be taken while planning trajectories that balance the multiple objectives. We observe that our approach maintains coverage performance as the number of samples taken considerably degrades. Further empirical results on different multi-agent problem setups demonstrate the applicability of our approach for both homogeneous and heterogeneous multi-agent teams. |
88 | Team Coordination on Graphs with State-Dependent Edge Costs | Limbu, Manshi, George Mason University Hu, Zechen, George Mason University Oughourli, Sara, George Mason University Wang, Xuan, George Mason University Xiao, Xuesu, George Mason University Shishika, Daigo, George Mason University | Keywords: Planning, Scheduling and Coordination, Cooperating Robots, Multi-Robot Systems | Abstract: This paper studies a team coordination problem in a graph environment. Specifically, we incorporate “support” action which an agent can take to reduce the cost for its teammate to traverse some high cost edges. Due to this added feature, the graph traversal is no longer a standard multi-agent path planning problem. To solve this new problem, we propose a novel formulation that poses it as a planning problem in a joint state space: the joint state graph (JSG). Since the edges of JSG implicitly incorporate the support actions taken by the agents, we are able to now optimize the joint actions by solving a standard single-agent path planning problem in JSG. One main drawback of this approach is the curse of dimensionality in both the number of agents and the size of the graph. To improve scalability in graph size, we further propose a hierarchical decomposition method to perform path planning in two levels. We provide both theoretical and empirical complexity analyses to demonstrate the efficiency of our two algorithms. |
89 | Incorporating Stochastic Human Driving States in Cooperative Driving between a Human-Driven Vehicle and an Autonomous Vehicle | Hossain, Sanzida, Oklahoma State University Lu, Jiaxing, Oklahoma State University Bai, He, Oklahoma State University Sheng, Weihua, Oklahoma State University | Keywords: Cooperating Robots, Intelligent Transportation Systems, Human Factors and Human-in-the-Loop | Abstract: Modeling a human-driven vehicle is a difficult subject since human drivers have a variety of stochastic behavioral components that influence their driving styles. We develop a cooperative driving framework to incorporate different human behavior aspects, including the attentiveness of a driver and the tendency of the driver following advising commands. To demonstrate the framework, we consider the merging coordination between a human-driven vehicle and an autonomous vehicle (AV) in a connected environment. We propose a stochastic model predictive controller (sMPC) to address the stochasticity in human driving behavior and design coordinated merging actions to optimize the AV input and influence human driving behavior through advising commands. Simulation and human-in-the-loop (HITL) experimental results show that our formulation is capable of accommodating a distracted driver and optimizing AV inputs based on human driving behavior recognition. |
90 | Epistemic Planning for Heterogeneous Robotic Systems | Bramblett, Lauren, University of Virginia Bezzo, Nicola, University of Virginia | Keywords: Cooperating Robots, Path Planning for Multiple Mobile Robots or Agents, Task and Motion Planning | Abstract: In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles. |
91 | Reinforced Potential Field for Multi-Robot Motion Planning in Cluttered Environments | Zhang, Dengyu, Sun Yat-Sen University Zhang, Xinyu, Sun Yat-Sen University Zhang, Zheng, Sun Yat-Sen University Zhu, Bo, Sun Yat-Sen University Zhang, Qingrui, Sun Yat-Sen University | Keywords: Multi-Robot Systems, Motion and Path Planning, Collision Avoidance | Abstract: Motion planning is challenging for multiple robots in cluttered environments without communication, especially in view of real-time efficiency, motion safety, distributed computation, and trajectory optimality, etc. In this paper, a reinforced potential field method is developed for distributed multi-robot motion planning, which is a synthesized design of reinforcement learning and artificial potential fields. An observation embedding with a self-attention mechanism is presented to model the robot-robot and robot-environment interactions. A soft wall-following rule is developed to improve the trajectory smoothness. Our method belongs to reactive planning, but environment properties are implicitly encoded. The total amount of robots in our method can be scaled up to any number. The performance improvement over a vanilla APF and RL method has been demonstrated via numerical simulations. Experiments are also performed using quadrotors to further illustrate the competence of our method. |
92 | Robot Team Data Collection with Anywhere Communication | Schack, Matthew, Colorado School of Mines Rogers III, John G., US Army Research Laboratory Han, Qi, Colorado School of Mines Dantam, Neil, Colorado School of Mines | Keywords: Multi-Robot Systems, Cooperating Robots, Path Planning for Multiple Mobile Robots or Agents | Abstract: Using robots to collect data is an effective way to obtain information from the environment and communicate it to a static base station. Furthermore, robots have the capability to communicate with one another, potentially decreasing the time for data to reach the base station. We present a Mixed Integer Linear Program that reasons about discrete routing choices, continuous robot paths, and their effect on the latency of the data collection task. We analyze our formulation, discuss optimization challenges inherent to the data collection problem, and propose a factored formulation that finds optimal answers more efficiently. Our work is able to find paths that reduce latency by up to 101% compared to treating all robots independently in our tested scenarios. |
93 | Coordination of Multiple Mobile Manipulators for Ordered Sorting of Cluttered Objects | Ahn, Jeeho, Korea University Lee, Sebin, Sogang University Nam, Changjoo, Sogang University | Keywords: Cooperating Robots, Multi-Robot Systems, Manipulation Planning | Abstract: We present a coordination method for multiple mobile manipulators to sort objects in clutter. We consider the object rearrangement problem in which the objects must be sorted into different groups in a particular order. In clutter, the order constraints could not be easily satisfied since some objects occlude other objects so the occluded ones are not directly accessible to the robots. Those objects occluding others need to be moved more than once to make the occluded objects accessible. Such rearrangement problems fall into the class of nonmonotone rearrangement problems which are computationally intractable. While the nonmonotone problems with order constraints are harder, involving with multiple robots requires another computation for task allocation.,In this work, we aim to develop a fast method, albeit suboptimally, for the multi-robot coordination for ordered sorting in clutter. The proposed method finds a sequence of objects to be sorted using a search such that the order constraint in each group is satisfied. The search can solve nonmonotone instances that require temporal relocation of some objects to access the next object to be sorted. Once a complete sorting sequence is found, the objects in the sequence are assigned to multiple mobile manipulators using a greedy task allocation method. We develop four versions of the method with different search strategies. In the experiments, we show that our method can find a sorting sequence quickly (e.g., 4.6 sec with 20 objects sorted into five groups) even though the solved instances include hard nonmonotone ones. The extensive tests and the experiments in simulation show the ability of the method to solve the real-world sorting problem using multiple mobile manipulators. |
94 | MOTLEE: Distributed Mobile Multi-Object Tracking with Localization Error Elimination | Peterson, Mason B., Massachusetts Institute of Technology Lusk, Parker C., Massachusetts Institute of Technology How, Jonathan, Massachusetts Institute of Technology | Keywords: Distributed Robot Systems, Visual Tracking, Localization | Abstract: We present MOTLEE, a distributed mobile multi-object tracking algorithm that enables a team of robots to collaboratively track moving objects in the presence of localization error. Existing approaches to distributed tracking make limiting assumptions regarding the relative spatial relationship of sensors, including assuming a static sensor network or that perfect localization is available. Instead, we develop an algorithm based on the Kalman-Consensus filter for distributed tracking that properly leverages localization uncertainty in collaborative tracking. Further, our method allows the team to maintain an accurate understanding of dynamic objects in the environment by realigning robot frames and incorporating frame alignment uncertainty into our object tracking formulation. We evaluate our method in hardware on a team of three mobile ground robots tracking four people. Compared to previous works that do not account for localization error, we show that MOTLEE is resilient to localization uncertainties, enabling accurate tracking in distributed, dynamic settings with mobile tracking sensors. |
95 | Dynamic Object Tracking for Quadruped Manipulator with Spherical Image-Based Approach | Zhang, Tianlin, Harbin Institute of Technology Guo, Sikai, Harbin Institute of Technology Xiong, Xiaogang, Harbin Institute of Technology, Shenzhen Li, Wanlei, Harbin Institute of Technology(ShenZhen) Qi, Zezheng, Harbin Institute of Technology, Shenzhen Lou, Yunjiang, Harbin Institute of Technology, Shenzhen | Keywords: Legged Robots, Visual Servoing, Visual Tracking | Abstract: Exactly estimating and tracking the motion of surrounding dynamic objects is one of important tasks for the autonomy of a quadruped manipulator. However, with only an onboard RGB camera, it is still a challenging work for a quadruped manipulator to track the motion of a dynamic object moving with unknown and changing velocities. To address this problem, this manuscript proposes a novel image-based visual servoing (IBVS) approach consisting of three elements: a spherical projection model, a robust super-twisting observer, and a model predictive controller (MPC). The spherical projection model decouples the visual error of the dynamic target into linear and angular ones. Then, with the presence of the visual error, the robustness of the observer is exploited to estimate the unknown and changing velocities of the dynamic target without depth estimation. Finally, the estimated velocity is fed into the model predictive controller (MPC) to generate joint torques for the quadruped manipulator to track the motion of the dynamical target. The proposed approach is validated through hardware experiments and the experimental results illustrate the approach's effectiveness in improving the autonomy of the quadruped manipulator. |
96 | Proprioception and Tail Control Enable Extreme Terrain Traversal by Quadruped Robots | Yang, Yanhao, Oregon State University Norby, Joseph, Apptronik Yim, Justin K., University of Illinois Urbana-Champaign Johnson, Aaron M., Carnegie Mellon University | Keywords: Legged Robots, Biologically-Inspired Robots, Optimization and Optimal Control | Abstract: Legged robots leverage ground contacts and the reaction forces they provide to achieve agile locomotion. However, uncertainty coupled with contact discontinuities can lead to failure, especially in real-world environments with unexpected height variations such as rocky hills or curbs. To enable dynamic traversal of extreme terrain, this work introduces 1) a proprioception-based gait planner for estimating unknown hybrid events due to elevation changes and responding by modifying contact schedules and planned footholds online, and 2) a two-degree-of-freedom tail for improving contact-independent control and a corresponding decoupled control scheme for better versatility and efficiency. Simulation results show that the gait planner significantly improves stability under unforeseen terrain height changes compared to methods that assume fixed contact schedules and footholds. Further, tests have shown that the tail is particularly effective at maintaining stability when encountering a terrain change with an initial angular disturbance. The results show that these approaches work synergistically to stabilize locomotion with elevation changes up to 1.5 times the leg length and tilted initial states. |
97 | Run and Catch: Dynamic Object-Catching of Quadrupedal Robots | You, Yangwei, Institute for Infocomm Research Liu, Tianlin, Peking University Liang, Xiaowei, Beijing Xiaomi Mobile Software Co., Ltd Xu, Zhe, Beijing Institute of Technology Zhou, Mingliang, Beijing Xiaomi Mobile Software Co., Ltd Li, Zhibin (Alex), University College London Zhang, Shiwu, University of Science and Technology of China | Keywords: Legged Robots, Whole-Body Motion Planning and Control, Climbing Robots | Abstract: Quadrupedal robots are performing increasingly more real-world capabilities, but are primarily limited to locomotion tasks. To expand their task-level abilities of object acquisition, i.e., run-to-catch as frisbee catching for dogs, this paper developed a control pipeline using stereo vision for legged robots which allows for dynamic catching balls while the robot is in motion. To achieve high-frame-rate tracking, we designed a ball that can actively emit homogeneous infrared (IR) light and then located the flying ball based on binocular vision positioning using the onboard RealSense D450 camera with an additional IR bandpass filter. The camera was mounted on top of a 2-DoF head to gain a full view of the target ball. A state estimation module was developed to fuse the vision positioning, camera motor readings, localization result of RealSense T265 equipped on the back, and the legged odometry output altogether. With the use of a ballistic model, we achieved a robust estimation of both the ball and robot positions in an inertial coordinate. Additionally, we developed a close-loop catching strategy and employed trajectory prediction so that tracking and run-to-catch were performed simultaneously, which is critical for such drastically dynamic and precise tasks. The proposed approach was validated through both static testing and dynamic catch experiments conducted on the CyberDog robot with a high success rate. |
98 | A Composite Control Strategy for Quadruped Robot by Integrating Reinforcement Learning and Model-Based Control | Lyu, Shangke, Nanyang Technological University Zhao, Han, Beijing University of Posts and Telecommunications Wang, Donglin, Westlake University | Keywords: Legged Robots, Motion Control, Reinforcement Learning | Abstract: Locomotion in the wild requires the quadruped robot to have strong capabilities in adaptation and robustness. The deep reinforcement learning (DRL) exhibits the huge potential in environmental adaptability, while its stability issues remain open. On the other hand, the quadruped robot dynamic model contains a lot of useful information that is beneficial to the robust control. The combination of DRL with model-based control may take both strengths and hold promises in better robustness. In this paper, the DRL and the proposed model-based controller are firmly integrated in a novel manner such that the proposed model-based controller is able to rectify the gait commands generated by DRL based on the system dynamic model so as to enhance the robustness of the quadruped robot against the external disturbances. Besides, a potential energy function is introduced to achieve the compliant contact. The stability of the proposed method is ensured in terms of passivity analysis. Several physical experiments are carried out to verify the performance of the proposed method. |
99 | Load Awareness: Sensorless Body Payload Sensing and Localization for Heavy Quadruped Robot | Liu, Shaoxun, Shanghai Jiao Tong University Zhou, Shiyu, Shanghai Jiao Tong University Pan, Zheng, Shanghai Jiao Tong University Niu, Zhihua, Shanghai Jiao Tong University Wang, Rongrong, Shanghai Jiao Tong University | Keywords: Legged Robots, Contact Modeling, Dynamics | Abstract: Heavy quadrupedal drives have great potential for overcoming obstacles, showing great possibilities for transportation industries in complex environments. Ground reaction force (GRF) is a crucial state variable for quadrupedal control. Most GRF observations are implemented in lightweight quadrupeds, with little consideration of the loading being static or slippery on the body. However, the load information is vital to the heavy-duty quadruped applied in transportation tasks. In this paper, we disassembled the whole-body dynamics into the body dynamics combined with the individual floating single-leg dynamics and completed observing the virtual coupling effects between the body and legs. Based on the observed coupling force and centroidal dynamics (CD), the GRF of a stance leg is obtained without the awareness of body weight, movement, and load information. Furthermore, we utilized the body dynamics and the observed virtual force to obtain the body's unknown payload. By reconstructing the moment balance equation, we obtained the payload's position concerning the body coordinate. Compared to conventional quadrupedal GRF observation methods, this framework achieves higher observation accuracy in heavy quadrupeds without load and body information. Additionally, it enables real-time calculation of load magnitude and position. |
100 | Evolutionary-Based Online Motion Planning Framework for Quadruped Robot Jumping | Yue, Linzhu, The Chinese University of Hong Kong Song, Zhitao, The Chinese University of Hong Kong Zhang, Hongbo, The Chinese University of Hong Kong Zhang, Lingwei, Hong Kong Centre for Logistics Robotics Zeng, Xuanqi, Chinese University of Hong Kong Liu, Yunhui, Chinese University of Hong Kong | Keywords: Legged Robots, Whole-Body Motion Planning and Control, Motion and Path Planning | Abstract: Offline evolutionary-based methodologies have supplied a successful motion planning framework for the quadrupedal jump. However, the time-consuming computation caused by massive population evolution in offline evolutionary-based jumping framework significantly limits the popularity in the quadrupedal field. This paper presents a time-friendly online motion planning framework, based on meta-heuristic Differential evolution (DE), Latin hypercube sampling, and Configuration space (DLC). The DLC framework establishes a multidimensional optimization problem leveraging centroidal dynamics to determine the ideal trajectory of the center of mass (CoM) and ground reaction forces (GRFs). The configuration space is introduced to the evolutionary optimization in order to condense the searching region. Latin hypercube sampling offers more uniform initial populations of DE under limited sampling points, which accelerates away from a local minimum. This research also constructs a collection of pre-motion trajectories as a warm start, when the objective state is in the neighborhood of the pre-motion state, to drastically reduce the solving time. The proposed methodology is successfully validated via real robot experiments for online jumping trajectory optimization with different jumping motions (e.g., ordinary jumping, flipping, and spinning). |