WELCOME KIT - ICRA 2026��IEEE Technical Committee on Rehabilitation and Assistive Robotics
Prepared by Rehabilitation and Assistive Robotics Student Representatives:�Jose Ben - Christian Mele
Table of Contents
Announcements 3
Meet the Team 4�Conference Venue 8�Floor Plan 9�Program at a glance 10�Day summaries � Monday, June 1st 11� Tuesday, June 2nd 12� Wednesday, June 3rd 15� Thursday, June 4th 17 & 18� Friday, June 5th 19
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Announcements
If you have any suggestion regarding this document, please leave a comment.
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Meet The Rehabilitation and Assistive Robotics
TC Chairs
Welcome to the ICRA 2026 Rehabilitation and Assistive Robotics Welcome Kit!
Tetsuyou Watanabe
Tetsuyou Watanabe is a professor with Kanazawa University. He received the B.S., M.S., and Dr.Eng. degrees in mechanical engineering from Kyoto University, Kyoto, Japan, in 1997, 1999, and 2003, respectively. From 2003 to 2007, he was a Research Associate with the Department of mechanical Engineering, Yamaguchi University, Japan. From 2007 to 2011, he was an assistant professor with Division of Human and Mechanical Science and Engineering, Kanazawa University. From 2011 to 2018, he was an associate professor with Faculty of Mechanical Engineering, Institute of Science and Engineering, Kanazawa University. Since 2018, he has been a professor with Kanazawa University. From 2008 to 2009, he was a visiting researcher at Munich University of Technology. His current research interests include robotic hand, grasping, object manipulation, medical and welfare sensors, surgical robots, and user interface. He got several awards including best paper award at Transactions of the Society of Instrument and Control Engineers and Best Paper Award on Robot Mechanisms and Design Finalist at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Currently, he serves as CO-chair of TC on Rehabilitation and assistive robotics in IEEE RAS, GC of IEEE-RAS International Conference on Soft Robotics (RoboSoft 2026), and GC of RSJ2026.
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Meet The Rehabilitation and Assistive Robotics
TC Chairs
Francesca Cordella
Prof. Francesca Cordella is currently Associate Professor of Bioengineering at Campus Bio-Medico University of Rome. Her research interests are mainly in the field of biomechanics, rehabilitation and assistive robotics, bionics, bio-inspired robot control strategies, human-robot interaction, multimodal interfaces for assistive and collaborative robotics, psychophysiological assessment, limb prostheses, sensory feedback, functional electrical stimulation.
She was and is involved in more than 25 European and national projects within her research fields. She is member of the Technical and Scientific Program Committee and Associate Editor for several International Conferences, Workshops and Journals. She has authored 3 patents and more than 120 peer-reviewed publications appeared in international journals, books and conference proceedings.
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Meet The Rehabilitation and Assistive Robotics
TC Chairs
Edward Brown
Dr. Edward E. Brown, Jr. received his B.S. in Electrical Engineering from the University of Pennsylvania, and his M.S. and Ph.D. degrees in Electrical Engineering both from Vanderbilt University. Dr. Brown is an associate faculty member in the Department of Electrical and Microelectronic Engineering at the Rochester Institute of Technology. He is also the director of the Biomechatronic Learning Laboratory. His area of research is in the field of Rehabilitation Robotics, which is the study of how robots can be used to assist individuals with physical disabilities. Specifically, Dr. Brown is interested in multi-modal human-robot interaction, interfacing, and integration (or HRI3) for rehabilitation applications. His research activities center on developing more intelligent orthotics and wearable robotic systems that utilize human physical and physiological information. The goal is to design systems that aid individuals with diseases and disabilities that specifically affect the skeletal musculature of their upper-limb extremities, and thus impair their dexterity and mobility during reaching and grasping motions. These diseases and disabilities include muscular atrophying diseases such as Muscular Dystrophy, Polio/Post Polio Syndrome, Multiple Sclerosis, and various Spinal Cord Injuries including Central Cord Syndrome.
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Meet The Rehabilitation and Assistive Robotics Student TC Chairs
Welcome to the ICRA 2026 Rehabilitation and Assistive Robotics Welcome Kit!
We are Jose and Christian - your new student Technical Committee Student Chairs. We are excited to act as your student representatives for IEEE Events and to help highlight interesting papers and projects in the space and amazing opportunities related to rehabilitation and assistive robotics going forward.
We are both incredibly passionate about rehabilitation robotics, and patient outcomes, so if you’re ever curious to hear about our work, or want to reach out with ideas of how we can make the TC better for graduate and undergraduate students we’d love to hear your opinion! You can reach us at:
Jose Ben: joseben@ieee.org
Christian Mele: cmele@uwaterloo.ca
Jose Ben
Christian Mele
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Dates: 1–5 June, Vienna, Austria
Location: VIECON – Vienna Congress & Convention Center
*Note: the old name is “Messe Wien” – you will find that name still on some signs and also the Viennese still refer to this name
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Conference Venue
A detailed, interactive map of the entrances, parking, transportation, and nearby restaurants and hotels can be found on VIECON’s website homepage.
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Floor Plan
Congress Center
Foyer A
Hall B
Hall A
Hall C
Hall D
Hall A
Hall B
Hall C
Foyer A
Mall
Program at a glance
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Monday, 1 June
Keynotes
No relevant keynote this day�
Workshops
No relevant keynote this day�
Oral Sessions
No relevant oral presentation this day�
Social Events
TC Meet-up : 8:00 PM
The IEEE Robotics & Automation Society’s Technical Committees for TCs on Cognitive Robotics, Rehabilitation and Assistive Robotics, and Human-Robot Interaction and Coordination are hosting a joint happy hour. This is a great opportunity to network, share ideas, and connect with members across our communities!
Time: 20:00 (after the ICRA Welcome Reception; attendees will be asked to arrive before 20:30)
Venue: Prater Alm (Prater 71B, 1020 Wien, Austria - 5 mins walking from VIECON)
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Tuesday, 2 June
Keynotes
Towards Wearable Robotics with better Portability, Safety, and Comfort
Workshops
No relevant keynote this day�
Oral Sessions
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Tuesday, 2 June
Keynotes
Towards Wearable Robotics with better Portability, Safety, and Comfort
Workshops
No relevant keynote this day�
Oral Sessions
10. Plantar Compensation Via Dynamic Control of Pneumatic Insoles for Flatfoot Deformity.
11. Point Cloud-Based Grasping for Soft Hand Exoskeleton
12. HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
13. Multi-Modal Locomotion Mode Recognition in the Real World for Robotic Hip Complex Exoskeletons
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Tuesday, 2 June
Keynotes
Towards Wearable Robotics with better Portability, Safety, and Comfort
Workshops
No relevant keynote this day�
Oral Sessions
18. Superelastic Tendon-Like Bowden Cables: Advancing Assistive Exoskeletons
19. Muscle Fatigue-Aware Controller for a Semi-Rigid Knee Exoskeleton (I)
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Wednesday June 3, 2026
Keynotes
No relevant oral presentation this day�
Workshops
No relevant oral presentation this day�
Oral Sessions
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Wednesday June 3, 2026
Keynotes
No relevant oral presentation this day�
Workshops
No relevant oral presentation this day�
Oral Sessions
10. Empirical Prediction of Pedestrian Comfort in Mobile Robot–Pedestrian Encounters
11. Symmetry-Aware Skill Transfer with Energy-Tank Passive Control for Ankle Exoskeletons
12.Learning Therapist Policy from Therapist-Exoskeleton-Patient Interaction
15. A 2-DoF Ankle Rehabilitation Platform Based on an Inclined Dual-Cylinder Mechanism
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Thursday June 4, 2026
Keynotes
No relevant oral presentation this day�Workshops
No relevant oral presentation this day�Oral Sessions
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Thursday June 4, 2026
Keynotes
No relevant oral presentation this day�Workshops
No relevant oral presentation this day�Oral Sessions
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Friday, 5 June 2026
Keynotes
No relevant oral presentation this day�
Workshops
Tailored to Move: Wearable Robotics for Motion Assistance
Oral Sessions
No relevant oral presentation this day�
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Tuesday June 2, 2026
Oral Presentation
Real-Time Sit-To-Stand Phase Classification with a Mobile Assistive Robot from Close Proximity Utilizing 3D Visual Skeleton Recognition
Location: Interactive Session, Hall C
Time: 09:00-10:30
Sit-to-stand (STS) transfer is a fundamental but challenging movement that plays a vital role in older adults' daily activities. The decline in muscular strength and coordination ability can result in difficulties performing STS and, therefore, the need for mobility assistance by humans or assistive devices. Robotics rollators are being developed to provide active mobility assistance to older adults, including STS assistance. In this paper, we consider the robotic walker SkyWalker, which can provide active STS assistance by moving the handles upwards and forward to bring the user to a standing configuration. In this context, it is crucial to monitor if the user is performing the STS and adapt the rollator's control accordingly. To achieve this, we utilized a standard vision-based method for estimating the human pose during the STS movement using Mediapipe pose tracking. Since estimating a user's state from extreme proximity to the camera is challenging, we compared the pose identification results from Mediapipe to ground truth data obtained from Vicon marker-based motion capture to assess accuracy and reliability of the STS motion. The fourteen kinematic features critical for accurate pose estimation were selected based on literature review and the specific requirements of our robot's STS method. By employing these features, we have implemented a phase classification system that enables the SkyWalker to classify the user's STS phase in real-time. The selected kinematics from vision-based human state estimation method and trained classifier can be furthermore generalized to other types of motion support, including adaptive STS path planning and emergency stops for safety insurance during STS.
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Tuesday June 2, 2026
Oral Presentation
GenJAPNet: A Generalizable Joint Angle Prediction Network with Non-Redundant Muscle Synergy Features for Lower-Limb Exoskeletons
Location: Interactive Session, Hall C
Time: 09:00-10:30
Lower-limb exoskeleton robots play a significant role in both rehabilitation and assisted walking, where accurate prediction of lower-limb joint angles is crucial for achieving natural gait. However, due to inter-subject variability and differences across locomotion modes, achieving cross-task generalization in joint angle prediction remains a major challenge. This work proposes a novel framework for multi-joint angle prediction in the lower-limb, which includes a non-redundant muscle synergy feature extraction algorithm and a Generalizable Joint Angle Prediction Network (GenJAPNet) across speeds and subjects. The feature extraction algorithm employs Non-negative Matrix Factorization (NMF) to extract activation coefficient matrix from Surface Electromyography (sEMG) signals, followed by further dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) to obtain more discriminative and non-redundant features. GenJAPNet leverages pre-trained shared features and few-shot fine-tuning to rapidly adapt to new task. Through feature extraction algorithm comparison experiments, cross-speed and cross-subject experiments, and exoskeleton-assisted walking physical experiments, the effectiveness and generalizability of this method are validated, demonstrating its potential for enhancing the performance of lower-limb exoskeleton rehabilitation and assistive applications.
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Tuesday June 2, 2026
Oral Presentation
DRL-SFM: Learning Social Navigation from Costmaps and Social Forces for Mobile Robots and Intelligent Wheelchairs
Location: Interactive Session, Hall C
Time: 09:00-10:30
The demand for assistive robots for passenger transport, such as intelligent wheelchairs, is increasing rapidly due to demographic changes. To allow passengers to navigate in crowded environments, such as shopping malls and hospitals, these systems must navigate in a socially accepted manner that ensures the comfort of both passengers and surrounding pedestrians. Although deep reinforcement learning (DRL) has shown promising results for social navigation, existing planners often learn overly passive behaviors, not engaging in the mutual adaptation characteristic of human interaction. In this paper, we introduce a novel DRL-based local planner that learns navigation behaviors by integrating the Social Force Model (SFM) directly into its reward function, allowing more cooperative interactions for mobile robots and intelligent wheelchairs. This approach encourages the agent to learn more forward-looking and mutual navigation policies by rewarding actions that align with the dynamics of pedestrians. To ensure generalization and straightforward deployment, our method utilizes the standard Navigation 2 local costmap augmented with pedestrian detections as an observation. The experiments demonstrate that our agent achieves a higher success rate in crowded scenarios with fewer space intrusions, outperforming the state-of-the-art DRL planner based on velocity obstacles by up to 11%.
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Tuesday June 2, 2026
Oral Presentation
Bioinspired Origami Exosuit for Sequential Lifting Assistance with Energy-Aware Compliance and Event-Triggered Impedance
Location: Interactive Session, Hall C
Time: 09:00-10:30
Back injuries resulting from manual material handling have long constituted a prominent threat to occupational safety. While back-support exosuits offer the potential to augment human strength, their practical implementation is hindered by persistent challenges pertaining to comfort and safety. Drawing inspiration from human biomechanics and muscle behavior, we develop a lightweight assistive exosuit that synchronizes with natural load-handling rhythms. By integrating a deployable Kresling origami structure with a twostage transmission mechanism, a single motor can sequentially assist both the waist and arms, achieving motion-conforming support with minimal complexity. An energy-aware compliance control strategy allows the system to yield passively during unassisted motion, avoiding interference with voluntary human behavior. We propose an event-triggered impedance control strategy based on an energy tank framework, which adaptively intervenes only when interaction energy exceeds safety thresholds. Experimental results demonstrate substantial reductions in muscle activation during load-handling tasks, with decreases of up to 22.8%, 15.4%, and 14.8% in the biceps, triceps, and erector spinae (MVC%), respectively.
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Tuesday June 2, 2026
Oral Presentation
Biarticular Rigid Powered Lower Extremity Exoskeleton Robot
Location: Interactive Session, Hall C
Time: 09:00-10:30
Lower extremity exoskeletons designed for multi-joint assistance are increasingly explored for rehabilitation and human augmentation. However, conventional monoarticular designs often suffer from joint misalignment and actuator redundancy, limiting their efficiency and user comfort. This study presents a biarticular rigid powered lower extremity exoskeleton that simultaneously assists the knee and ankle joints through a single actuator, enabling coordinated torque generation across adjacent joints. A hierarchical control framework combining gait segmentation, impedance-based torque generation, and gravity/friction compensation is implemented to provide phase-specific assistance. Experimental results show that the proposed exoskeleton reduces gastrocnemius activation by up to 63.4% and metabolic cost by up to 11.6% during stair ascent, with corresponding reductions of 28.3% and 8.2% during level walking. These findings demonstrate the effectiveness of the biarticular and underactuated structure in enhancing locomotor efficiency, highlighting its potential as a compact and practical solution for dynamic and diverse mobility scenarios.
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Tuesday June 2, 2026
Oral Presentation
Proactive Grasp Assistance in a Robotic Hand Exoskeleton Improves Performance and Preference in Challenging Tasks
Location: Interactive Session, Hall C
Time: 09:00-10:30
Advancements in perception, planning, and control, enable the development of wearable robots capable of proactively assisting users in avoiding potentially negative outcomes. However, the introduction of robotic assistance in general is often associated with a loss in the sense of agency, a factor traditionally associated with overall device acceptance. Recent work provides a different perspective, showing that contextual proactive assistance is well-received for teleoperation or shared workspace tasks. Still, no works have investigated the impact of proactive assistance for wearable grasping devices, where physical interactions have increased potential for disrupting the user's experience. In this study, we analyze the impact of proactive assistance in a hand exoskeleton with an abstracted grasping task of varying difficulty. We show that in general, the presence of assistance does not significantly reduce experience or the sense of agency. In fact, in a difficult task, subjects strongly prefer proactive assistance, likely as a result of its provided utility. When the task is easily completed without assistance, subjects indicate no strong preference for assisted conditions. Our results challenge the notion of a direct trade-off between robotic assistance and agency, suggesting that well-designed assistance can improve performance and user preference without compromising their sense of control.
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Tuesday June 2, 2026
Oral Presentation
Mechanomyography-Based Closed-Loop Control of FES Enabling Prolonged Force Assistance by Monitoring Muscle Fatigue
Location: Interactive Session, Hall C
Time: 09:00-10:30
Functional Electrical Stimulation (FES) is a critical therapy for motor rehabilitation, yet the rapid onset of muscle fatigue severely limits its efficacy. This paper presents the design, implementation, and validation of a comprehensive, intelligent closed-loop FES system designed to provide effective force assistance by actively sensing FES-induced fatigue. The system integrates a pressure-based Mechanomyography (P_MMG) sensor for real-time feedback of muscle force capacity, a Kalman filter for robust signal estimation, and a fuzzy-logic-based Proportional-Integral-Derivative (PID) controller to modulate FES dynamically. The developed system was first validated in a comprehensive simulation and then tested with four healthy participants. The results demonstrate that the closed-loop fuzzy PID controller yielded a functionally meaningful improvement in performance over an open-loop-controlled protocol. The system substantially extended the duration of effective FES and, critically, delayed the onset of functional failure (indicated by a force drop >50%), with performance improvements showing a strong trend toward statistical significance (Wilcoxon signed-rank test, p = 0.0625). This work delivers a practical and effective solution for managing fatigue during FES therapy, holding the potential to significantly enhance rehabilitation outcomes.
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Tuesday June 2, 2026
Oral Presentation
Non-Motorized Hand Exoskeleton for Rescue and Beyond: Substantially Elevating Grip Endurance and Strength
Location: Interactive Session, Hall C
Time: 09:00-10:30
Robotic hand exoskeletons hold immense potential for enhancing human hand functionality, addressing the hand's strength limitations and fatigue during physically-demanding tasks. However, most existing hand exoskeletons are motorized, being weak in generating high supporting force for gripping augmentation. We present a nonmotorized hand exoskeleton based on magnetorheological (MR) actuators to provide high gripping support and elevate grip endurance. Meanwhile, it ingeniously harnesses human energy for actuation and energy storage, enhancing grip strength without external power. The MR actuator demonstrates a peak holding force of 1046 N with merely 5 W power input, boasting a force-to-power ratio one-order-of-magnitude higher than conventional approaches, and 97.7% energy reduction for same holding force compared to other approaches. Participants wearing the hand exoskeletons experience a 41.8% enhancement in grip strength without external power and reduced hand muscle fatigue during prolonged physical labor. In rescuing scenarios such as postearthquake rescue, debris clearance, and casualty evacuation, our exoskeleton effectively supports gripping and improves working efficiency.
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Tuesday June 2, 2026
Oral Presentation
Intent Recognition in Gait Transition Using Muscle Volume Sensors with Deep Learning
Location: Interactive Session, Hall C
Time: 09:00-10:30
Intention recognition is essential for wearable robotics and assistive systems. However, conventional approaches often suffer from cumbersome sensor setups or sensitivity to external disturbances. To address these limitations, this study proposes an LSTM-based intention recognition method using lower-limb Muscle-Volume (MV) sensors. An insole-type pressure sensor, an IMU sensor, and a cuff-type MV sensor were used to record a series of motions, including sitting, standing, walking, and running. Deep learning techniques were then applied for classification and transition detection. Accuracies of the predicted movement states based on data from the IMU, insole-type pressure, and cuff-type MV sensors were 93.04%, 97.65%, and 93.08%, respectively. The average transition detection latencies for the IMU, insole, and MV sensor model were 0.135 s, 0.377 s, and 0.455 s, respectively. Results show that the proposed MV sensor achieves performance comparable to insole pressure sensors, demonstrating its potential as a practical and robust alternative for intention recognition in wearable systems.
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Tuesday June 2, 2026
Oral Presentation
Plantar Compensation Via Dynamic Control of Pneumatic Insoles for Flatfoot Deformity
Location: Interactive Session, Hall C
Time:15:00-16:30
Human feet are crucial for supporting body weight and adapting to complex terrains. Adult-acquired flatfoot deformity (AAFD) arises from congenital or acquired causes, impairing the foot's ability to transition between flexible and rigid states, known as the lock-unlock mechanism during the stance and swing phases. In this study, we propose a plantar dynamic support system that utilizes pneumatic airbags, regulated through a model predictive control (MPC) strategy to minimize tracking errors. Experiments were conducted to measure kinetic parameters and electromyography signals, validating the system's efficacy. The results showed improvements in the normalized navicular height truncated (NNHt) index and reductions in muscle activity of the fibularis longus (FL), soleus (SOL), and gastrocnemius (GAST) by 4.42%, 16.65%, and 23.84%, respectively, during the stance phase.
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Tuesday June 2, 2026
Oral Presentation
Point Cloud-Based Grasping for Soft Hand Exoskeleton
Location: Interactive Session, Hall C
Time:15:00-16:30
Grasping is a fundamental skill for interacting with and manipulating objects in the environment. However, this ability can be challenging for individuals with hand impairments. Soft hand exoskeletons designed to assist grasping can enhance or restore essential hand functions, yet controlling these soft exoskeletons to support users effectively remains difficult due to the complexity of understanding the environment. This study presents a vision-based predictive control framework that leverages contextual awareness from depth perception to predict the grasping target and determine the next control state for activation. Unlike data-driven approaches that require extensive labelled datasets and struggle with generalizability, our method is grounded in geometric modelling, enabling robust adaptation across diverse grasping scenarios. The Grasping Ability Score (GAS) was used to evaluate performance, with our system achieving a state-of-the-art GAS of 91 ' 2% across 15 objects and healthy participants, demonstrating its effectiveness across different object types. The proposed approach maintained reconstruction success for unseen objects, underscoring its enhanced generalizability compared to learning-based models.
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Tuesday June 2, 2026
Oral Presentation
HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
Location: Interactive Session, Hall C
Time:15:00-16:30
The increasing labor shortage and aging population underline the need for assistive robots to support human care recipients. To enable safe and responsive assistance, robots require accurate human motion prediction in physical interaction scenarios. However, this remains a challenging task due to the variability of assistive settings and the complexity of coupled dynamics in physical interactions. In this work, we address these challenges through two key contributions: (1) HHI-Assist, a dataset comprising motion capture clips of human-human interactions in assistive tasks; and (2) a conditional Transformer-based denoising diffusion model for predicting the poses of interacting agents. Our model effectively captures the coupled dynamics between caregivers and care receivers, demonstrating improvements over baselines and strong generalization to unseen scenarios. By advancing interaction-aware motion prediction and introducing a new dataset, our work has the potential to significantly enhance robotic assistance policies. The dataset and code are available at https://sites.google.com/view/hhi-assist/home .
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Tuesday June 2, 2026
Oral Presentation
Multi-Modal Locomotion Mode Recognition in the Real World for Robotic Hip Complex Exoskeletons
Location: Interactive Session, Hall C
Time:15:00-16:30
Lower limb exoskeletons assist users by supporting joint movements. Since joint motion patterns vary depending on how the user moves, accurately recognizing the type of movement (locomotion mode) is crucial for controlling the exoskeleton and ensuring user safety. Inspired by how humans use multiple types of sensory information to control movement, we developed a multi-modal locomotion mode recognition (LMR) system that uses both mechanical and visual sensor data to identify locomotion modes. Our approach utilizes two fusion methods: intermediate fusion, which combines the data in the form of features, and late fusion, which integrates the sensor data by averaging the recognition results from each sensor. By fusing these two different modalities, the prediction accuracy improved by an average of 11.7% with the test data. Through comparisons with uni-modal LMR systems that rely on a single type of sensor data for locomotion mode recognition, we found that the improved performance of the multi-modal LMR system is due to the visual information's ability to generalize different gait patterns across users and the mechanical sensor data's consistency within the same classes.
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Tuesday June 2, 2026
Oral Presentation
Voice-Driven Assistance and Resistance Modulation in a Soft Hip Exosuit Using a Transformer-Based Speech Recognition Model
Location: Interactive Session, Hall C
Time:15:00-16:30
Intuitive human'robot interfaces are essential to increase usability and personalization in wearable robotic assistive technologies. However, most current systems rely on pre-programmed or sensor-driven strategies that offer limited active user control online. To address this limitation, we present a voice-driven control framework for a soft hip exosuit, enabling on-demand modulation of assistance and resistance via short spoken commands. The system combines a fully embedded transformer-based automatic speech recognition model (Whisper) with a gait-phase estimator to synchronize actuation with the user's motion. Users can switch between assistive and resistive modes and select discrete gain levels (low, medium, high). Experiments with six healthy participants demonstrate high recognition accuracy (95-100%) and low latency (∼9 ms). Metabolic measurements show that assistive commands reduced walking energy cost by 20.9'4.8% (LOW) and 9.7'5.5% (MEDIUM) relative to baseline, while resistive commands increased cost by 13.1'3.5% (MEDIUM) and 14.9'5.1% (HIGH). These results highlight the feasibility of intuitive, voice-driven modulation in wearable robotics.
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Tuesday June 2, 2026
Oral Presentation
Long-Horizon Planning with Large Language Models for Indoor Assistive Navigation of the Visually Impaired
Location: Interactive Session, Hall C
Time:15:00-16:30
For visually impaired individuals, assistive navigation systems play a crucial role in enabling independent mobility. However, long-horizon planning based on natural language (NL) instructions in complex indoor environments remains a significant challenge. Recent studies show the strong potential of Large Language Models (LLMs) in NL understanding and task-level planning. Yet, the inherent limitations of LLMs in mathematical reasoning and their susceptibility to hallucination hinder their reliability in low-level path planning. In this paper, we introduce an LLM-based indoor assistive navigation system that interprets NL instructions from visually impaired users for autonomous navigation. At its core is a novel planning agent that grounds instructions to the environment's topological map and generates optimal route plans. To avoid hallucination in geometric reasoning, the LLM handles only high-level semantic planning, while precise node-level paths are delegated to a classical graph search algorithm. We further implement a wearable assistive device that provides voice and vibrotactile feedback to deliver hands-free navigation. Offline evaluations and real-world experiments demonstrate that our system can reliably plan grounded routes and enable visually impaired users to autonomously complete long-horizon navigation tasks. Anonymous project page is available at https://lhp-ian.github.io.
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Tuesday June 2, 2026
Oral Presentation
EMG-Based Torque Prediction for Assistive Exoskeleton Control Using Neural Networks with Bounded Generalization Error
Location: Interactive Session, Hall C
Time:15:00-16:30
Electromyography (EMG) signals are widely used in assistive exoskeleton control for predicting human joint torque due to their ability to extract muscle activations before movement onset. The standard procedure for learning the EMG-to-torque model involves training the model on a training set of EMG-torque data, followed by validating the model on a separate test set. The comparison between models is generally undertaken on the test set. However, the analysis of model performance on the data outside the test set remains unaddressed. The lack of a guarantee for unseen data reduces the reliability of EMG-to-torque models in practical exoskeleton control. In this paper, we address this issue by proposing a bounded-generalization-error neural network (BGNN) for EMG-based torque prediction. Using gradient descent to train the network, we formulate at each training step a theoretical upper bound on the generalization error, reflecting the prediction error across the entire data distribution, including unseen data beyond the test set. The NN training is terminated when this upper bound reaches its minimum, thereby achieving the tightest guarantee on the generalization error. Experimental results on torque prediction demonstrated that, while ensuring such a bounded generalization error, our method still gave results comparable to those of classical models. The use of our BGNN in assistive exoskeleton control was also tested with 13 participants on a pick-and-place task with an upper limb exoskeleton. Experimental results on assistive control revealed that our method can reduce human physical fatigue without compromising movement speed or accuracy compared to natural human movement characteristics, particularly for generalization in novel tasks.
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Tuesday June 2, 2026
Oral Presentation
Closed-Loop Multimodal Sensory Training Enhances the Proprioceptive-Motor Pathway: Low-Load Automaticity and Fine Motor Control
Location: Interactive Session, Hall C
Time:15:00-16:30
Designing reliable upper-limb human-machine interfaces (HMIs) with low attentional demand requires strengthening the proprioceptive-motor pathway (PMP). We propose a closed-loop multimodal sensory training that maps three robot joint angles to six bidirectional electrotactile channels and combines visual fading with degrees-of-freedom (DoF) progression to shift reliance from vision to tactile-proprioceptive guidance. The objective is low-load automaticity for supplemental cues and improved native-limb fine motor control. Twenty right-handed adults completed a six-day protocol. Using synchronized kinematics and EEG, we evaluated electrotactile-driven tasks: eyes-closed continuous tracking and static posture reproduction, dual-task posture reproduction with serial subtraction, reversed-mapping generalization, and a proprioceptively constrained maze. Training produced robust gains under tactile-proprioceptive dominance: errors decreased (~30%) and response time shortened. Under dual-task load, posture error and response time decreased while correct subtractions increased and mistakes decreased, supporting low-load automaticity of electrotactile decoding. Although group-level β-event-related desynchronization (ERD) changes were not significant, contralateral ERD reductions and post-movement beta rebound (PMBR) enhancements during tactile decoding were consistent with reduced cortical effort and emerging automatic control. Performance generalized to reversed mapping, and maze completion time decreased significantly, evidencing improved fine motor control. These findings show that closed-loop vision-tactile-proprioceptive integration offers a compact, reproducible route to PMP enhancement, enabling low-load automaticity and finer control, with actionable design targets for prosthetics, exoskeleton rehabilitation, and vision-limited teleoperation.
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Tuesday June 2, 2026
Oral Presentation
Superelastic Tendon-Like Bowden Cables: Advancing Assistive Exoskeletons
Location: Interactive Session, Hall C
Time:15:00-16:30
This study introduces a novel Bowden cable (BC) system for hand-assistive exoskeletons employing superelastic (SE) shape memory alloy wires to address key limitations, such as efficiency and safety limitations. The unique properties of SE wires enable a single-wire transmission, offering enhanced performance, plus inherent self-sensing and self-limiting capabilities that provide tendon-like overload protection. Experimental results obtained with a setup simulating use conditions demonstrate the superior efficiency of SE wires, with 1/4 the friction of conventional steel cables. In addition, a validated force-sensing capability, achieved by monitoring electrical resistance, proves to accurately detect overload within 1% force error. This, along with the inherent passive force self-limiting behaviour during simulated collisions, demonstrates the ability of the SE BC to effectively mimic the protective function of biological tendons. Therefore, this biomimetic innovation in soft robotic transmission significantly improves safety and efficiency, presenting a promising advancement for human-robot interaction in assistive and rehabilitative robotics.
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Tuesday June 2, 2026
Oral Presentation
SA-VLM V2: Useful, Comprehensive, and Concise Guidance for Guide-Dog Robots Assisting the Visually Impaired
Location: Interactive Session, Hall C
Time:15:00-16:30
The development of guide dog robots is expected to enhance the mobility and safety of visually impaired individuals outdoors. To assist these users in real-world navigation, walking guidance should be useful, comprehensive, and concise so that instructions are both actionable and easy to follow. While recent VLMs show promising capabilities in scene understanding, existing approaches do not address the effective delivery of guidance for visually impaired users.<p>In this work, we propose SA-VLMv2 (Space-Aware VLM), a model designed to generate useful, comprehensive, and concise walking guidance based on ego-centric scenes and target destinations. To this end, we first derived four canonical templates for walking guidance through user evaluation with professional guide dog trainers across diverse images, providing insights into preferred guidance formats. We then collected, manually annotated, curated a dataset of 19,945 samples aligned with these templates and trained SA-VLMv2 from the open-sourced VLM, Qwen2.5VL. Experimental results show that SA-VLMv2 outperforms state-of-the-art proprietary MLLMs (Claude 3.5 Sonnet, Gemini 2.5, GPT-4o) and the open-sourced pretrained VLM (Qwen2.5VL) in both holistic and factor-wise evaluations. SA-VLMv2 generated more concise yet informative guidance while achieving higher scores across multiple evaluation factors.
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Wednesday June 3, 2026
Oral Presentation
A 3-Degrees-Of-Freedom Lightweight Flexible Twisted String Actuators (TSAs)-Based Exoskeleton for Wrist Rehabilitation
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: This paper introduces a lightweight, three-degrees- of-freedom exoskeleton for wrist rehabilitation powered by Twisted String Actuators (TSAs), specifically designed to support flexion/extension, radial/ulnar deviation, and pronation/supination movements. Leveraging the high power-to-weight ratio of TSA actuation system, the exoskeleton ensures effective, comfortable, and personalized rehabilitation exercises. The device comprises five TSAs arranged in a tendon-driven configuration, enabling precise control and adaptability to various user anatomies. The experimental evaluations was conducted on a prototype demonstrating the device's ability to accurately replicate wrist movements guided by a physiotherapist, achieving low tracking errors (RMSE 1'). The exoskeleton effectively achieves the desired wrist range of motion'115' for flexion/extension, 70' for radial/ulnar deviation, and 150' for pronation/supination'with torque capabilities suitable for rehabilitation purposes (0.35 Nm for flexion/extension and radial/ulnar<p>deviation, and 0.06 Nm for pronation/supination). These preliminary results validate the exoskeleton as a promising solution, offering improved comfort, flexibility, and effectiveness compared to traditional rehabilitation devices.
39
Wednesday June 3, 2026
Oral Presentation
Design of a Novel Loosely Coupled Parallel Structural Upper Body Exoskeleton
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Exoskeletons, as wearable human'robot collaborative devices, can effectively reduce muscle fatigue caused by prolonged material handling and overhead tasks. However, most existing active exoskeletons adopt tightly coupled serial structures, which generally suffer from insufficient wearing comfort, limited muscle coverage, and restricted workspace. To address these issues, this paper presents a novel loosely coupled, parallel upper-body exoskeleton (6.9kg). The proposed exoskeleton is connected only at the waist and elbow, providing assistance not only to the small muscle groups of the arms and shoulders but also to the larger muscle groups of the waist, back, and chest. Moreover, heavy components of the exoskeleton (approximately 78% of the total mass), such as actuators are located near the wearer's waist, which places the center of mass close to the human center of mass, improving comfort and control reliability. To validate the feasibility of the design, kinematic models of both the exoskeleton and the human upper body were established. Analysis showed that the end-effector workspace of the exoskeleton exceeds that of the human elbow. Prototype experiments were conducted, allowing the wearer to perform arbitrary postures without constraining spinal motion. This indicates that the exoskeleton holds potential in work assistance scenarios such as long-term heavy lifting and overhead work.
40
Wednesday June 3, 2026
Oral Presentation
A Non-Invasive Closed-Loop Myoelectric Prosthetic Hand Featuring Electrotactile Sensory Feedback
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: The absence of sensory feedback has been a critical challenge for myoelectric prostheses in recent years. While electrotactile feedback has emerged as an effective non-invasive solution, significant challenges remain in simultaneously ensuring real-time performance, processing EMG signals under electrical stimulation interference, and transmitting richer sensory information. This study proposes a multidimensional bio-inspired electrical stimulation feedback paradigm, implemented on a self-developed closed-loop myoelectric prosthetic hand system with real-time interference avoidance capability. Utilizing the human cutaneous nervous system as the feedback pathway, our paradigm establishes diverse electrotactile patterns through real-time modulation of four-channel stimulation parameters (frequency and current intensity). Experimental results with both able-bodied participants and amputees demonstrate that the proposed paradigm can accurately convey prosthetic state information, enabling users to perceive object size, length, shape, and stiffness through the prosthetic hand. This feedback framework provides a viable sensory restoration solution for prosthetic applications.
41
Wednesday June 3, 2026
Oral Presentation
Self-Wearing Adaptive Garments Via Soft Robotic Unfurling
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Robotic dressing assistance has the potential to improve the quality of life for individuals with limited mobility. Existing solutions predominantly rely on rigid robotic manipulators, which have challenges in handling deformable garments and ensuring safe physical interaction with the human body. Prior robotic dressing methods require excessive operation times, complex control strategies, and constrained user postures, limiting their practicality and adaptability. This paper proposes a novel soft robotic dressing system, the Self-Wearing Adaptive Garment (SWAG), which uses an unfurling and growth mechanism to facilitate autonomous dressing. Unlike traditional approaches, the SWAG conforms to the human body through an unfurling-based deployment method, eliminating skin-garment friction and enabling a safer and more efficient dressing process. We present the working principles of the SWAG, introduce its design and fabrication, and demonstrate its performance in dressing assistance. The proposed system demonstrates effective garment application across various garment configurations, presenting a promising alternative to conventional robotic dressing assistance.
42
Wednesday June 3, 2026
Oral Presentation
A Physiotherapy Video Matching Method Supporting Arbitrary Camera Placement Via Angle-Of-Limb-Based Posture Structures
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: The 'Hospital at Home' initiative transforms medical service automation through modern technologies. This paper revisits remote physiotherapy, allowing convalescents to record exercises using mobile devices from arbitrary angles. To address this, we propose a physiotherapy video matching method that accurately aligns movements from unconstrained viewpoints. The task is formulated as an optimization problem and solved using a modular pipeline. We introduce the Angle-of-Limb-based Posture Structure (ALPS) and the Camera-Angle-Free (CAFE) transformation to counter camera-angle differences. We also develop the Three-phase ALPS Matching Algorithm (TALMA) for matching movements between mentor and convalescent videos. Real-world experiments show our method outperforms existing solutions in both precision and practicality, with a time deviation of less than 0.07 seconds from expert annotations. The prototype and datasets are publicly available at: https://github.com/NCKU-CIoTlab/TALMA-on-ALPS/.
43
Wednesday June 3, 2026
Oral Presentation
Clinicians Perspectives on Safety, Ethical, and Legal Considerations for Home-Based Physical Rehabilitation Robots
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: The growing demand for neurorehabilitation is driving the development of innovative, home-based robotic solutions, offering a promising approach to alleviate the strain on healthcare systems burdened by limited resources and workforce shortages. Despite significant technological advancements in rehabilitation robotics, adoption remains limited due to unresolved safety, legal, and ethical concerns. This study provides a comprehensive analysis of these three aspects from the perspective of experienced neurorehabilitation clinicians, offering valuable insights into the challenges surrounding home-based rehabilitation robots. Using a qualitative approach, we identified eight key themes derived from clinicians' feedback. These themes underscore critical areas, including the need for robust safety measures, regulatory clarity on liability and data privacy, and the ethical imperative of ensuring equitable access to technology for diverse user populations. Our findings highlight the need for a multifaceted approach to overcome these challenges, including user-centred design, rigorous testing, comprehensive user training, and necessary updates to regulatory frameworks to ensure the safe, effective, and equitable deployment of these technologies.
44
Wednesday June 3, 2026
Oral Presentation
DigiArm: An Anthropomorphic 3D-Printed Prosthetic Hand with Enhanced Dexterity for Typing Tasks
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Despite recent advancements, existing prosthetic limbs are unable to replicate the dexterity and intuitive control of the human hand. Current control systems for prosthetic hands are often limited to grasping, and commercial prosthetic hands lack the precision needed for dexterous manipulation or applications that require fine finger motions. Thus, there is a critical need for accessible and replicable prosthetic designs that enable individuals to interact with electronic devices and perform precise finger pressing, such as keyboard typing or piano playing, while preserving current prosthetic capabilities. This paper presents a low-cost, lightweight, 3D-printed robotic prosthetic hand, specifically engineered for enhanced dexterity with electronic devices such as a computer keyboard or piano, as well as general object manipulation. The robotic hand features a mechanism to adjust finger abduction/adduction spacing, a 2-D wrist with the inclusion of controlled ulnar/radial deviation optimized for typing, and control of independent finger pressing. We conducted a study to demonstrate how participants can use the robotic hand to perform keyboard typing and piano playing in real time, with different levels of finger and wrist motion. This supports the notion that our proposed design can allow for the execution of key typing motions more effectively than before, aiming to enhance the functionality of prosthetic hands.
45
Wednesday June 3, 2026
Oral Presentation
Estimation of Gait Phase of Human Stair Descent Walking Based on Phase Variable Approach
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Synchronization between a wearer and a lower limb powered prosthesis is important for effective control. Typically, phase variable-based phase estimation methods are employed. However, there is a noticeable lack of studies focusing on estimating the gait phase during stair descent, likely due to the difficulty in generating a reliable phase variable. In most studies, the thigh angle is used to generate phase variables for level walking because it follows a sinusoidal pattern. However, during stair descent, the thigh angle exhibits only a partially sinusoidal shape, making it challenging to apply the methods used for level walking. In this study, we propose a novel phase variable generation method to address the difficulty of using only the thigh angle for stair descent. To estimate the gait phase reliably, the phase variable is defined differently for the stance and swing phases: the hip position is used to generate the phase variable during the stance phase, and the thigh angle is used during the swing phase. These phase variables are then unified into a single phase variable (PV-ENT) for the entire gait cycle of stair descent. During this unification process, a non-smooth transition occurs around the phase transition point. To address this, a blending method is applied. The proposed method was validated using the data from 12 healthy subjects, collected through a motion capture system and IMU sensors. The results demonstrate a reliable phase estimation performance. Moreover, the blending method successfully improves the smoothness of the phase variable around the phase transition point without reducing the overall phase estimation performance.
46
Wednesday June 3, 2026
Oral Presentation
A 2-DoF Ankle Rehabilitation Platform Based on an Inclined Dual-Cylinder Mechanism
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: This paper presents a novel ankle rehabilitation platform based on an inclined dual-cylinder mechanism that provides 2-DoF motion through geometric coupling, without complex multi-link structures. Two cylinders sharing a 9° inclined contact surface are driven by two stepper motors, enabling simultaneous dorsiflexion/plantarflexion and inver sion/eversion of up to 18° in each axis. The platform provides both a passive mode, which follows predefined trajectories, and an active mode, which captures user intent through center-of pressure estimation using a force-sensing resistor–based insole. A Particle Swarm Optimization–tuned PD controller is used in both modes, achieving an RMS tracking error below 0.35°in experimental validation. An IMU-integrated gamification environment further demonstrates the feasibility of the platform as an interactive active training system.
47
Wednesday June 3, 2026
Oral Presentation
An Underwater Exoskeleton for Scuba Diving: Reducing Air Consumption and Muscle Activation through Knee Assistance
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Evolutionary pressures have pushed humans to become efficient walkers, but inefficient divers. People consume more energy to travel the same distance underwater than on land. In diverse overground locomotion, emerging exoskeletons have reduced the metabolic cost of humans. Can we also improve the energy economy in underwater locomotion via exoskeletons? Here, we propose an underwater exoskeleton to assist scuba diving using flutter kick, by applying assistive knee extension torque during the strike phase of the diving kick cycle. When divers wore the powered exoskeleton, the average net air cost across six experienced divers was reduced by 22.7±10.0%, and the peak quadriceps activation was decreased by 20.9±7.5%, compared with normal diving without the exoskeleton. The average gastrocnemius activation also decreased by 20.6±5.3%, suggesting that the divers sufficiently utilized the exoskeleton assistance. These results indicate that applying exoskeleton assistance is conducive to improving the endurance of human underwater diving and enhancing our ability to explore the underwater world. Our study extends the application boundary of wearable robots, and provides a reference for the
48
Wednesday June 3, 2026
Oral Presentation
A Semi-Active Occupational Shoulder Exoskeleton for Overhead Work with Free Mode and Personalized Assistive Torque
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Current passive or semi-active shoulder exoskeletons for overhead work provide fixed assistive torque for all participants and tasks, which lacks adaptability. In addition, due to the need to store energy at low elevation angles, they may increase physical demand on the user when assistance is not required. This study presents a novel semi-active shoulder exoskeleton that can provide the free mode (i.e., no assistance) and personalized assistive torque to assist overhead work. The exoskeleton includes the motorized torque generator and hybrid control strategy. The motorized torque generator equipped with servo motor and encoder is characterized by its ability to electrically adjust the peak assistive torque angle and peak torque. In addition, we propose a hybrid control strategy with free and assistive modes. The free mode allows the exoskeleton to not interfere with movements that do not require assistance. The assistive mode provides personalized torque with three levels based on the height and weight of the user. Experimental results validated the exoskeleton's mechanical performance (e.g., high backdrivability) and its assistive effectiveness. The results showed that the exoskeleton could reduce shoulder muscle activation by up to 55.03% and demonstrated a significant difference compared to fixed assistance.
49
Wednesday June 3, 2026
Oral Presentation
Learning Therapist Policy from Therapist-Exoskeleton-Patient Interaction
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Post-stroke rehabilitation is often necessary for patients to regain proper walking gait. However, the typical therapy process can be exhausting and physically demanding for therapists, potentially reducing therapy intensity, duration, and consistency over time. We propose a Patient-Therapist Force Field (PTFF) to visualize therapist responses to patient kinematics and a Synthetic Therapist (ST) machine learning model to support the therapist in dyadic robot-mediated physical interaction therapy. The first encodes patient and therapist stride kinematics into a shared low-dimensional latent manifold using a Variational Autoencoder (VAE) and models their interaction through a Gaussian Mixture Model (GMM), which learns a probabilistic vector field mapping patient latent states to therapist responses. This representation visualizes patient–therapist interaction dynamics to inform therapy strategies and robot controller design. The latter is implemented as a Long Short-Term Memory (LSTM) network trained on patient–therapist interaction data to predict therapist-applied joint torques from patient kinematics. Trained and validated using leave-one-out cross-validation across eight post-stroke patients, the model was integrated into a ROS-based exoskeleton controller to generate real-time torque assistance based on predicted therapist responses. Offline results and preliminary testing indicate the potential of their use as an alternative approach to post-stroke exoskeleton therapy. The PTFF provides understanding of the therapist’s actions while the ST frees the human therapist from the exoskeleton, allowing them to continuously monitor the patient’s nuanced condition.
50
Wednesday June 3, 2026
Oral Presentation
Symmetry-Aware Skill Transfer with Energy-Tank Passive Control for Ankle Exoskeletons
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: This paper presents a unified framework that combines symmetry-aware skill transfer with energy-tank passive control to achieve safe and adaptive ankle exoskeleton assistance. Subject-specific ankle references are first extracted from wearable IMU data : Dynamic Time Warping (DTW) aligns gait cycles onto a normalized phase axis , and Gaussian Mixture Regression (GMR) synthesizes smooth probabilistic templates suitable for online modulation. When only unilateral sensing is available, contralateral trajectories are reconstructed through either a half-period phase shift or a DTW-informed nonlinear mapping, enabling robust bilateral assistance. These references are then tracked by a joint-space PID controller wrapped with an energy tank, which bounds power exchange and prevents unintended energy injection. In simulation experiments, the proposed controller improved center-of-mass smoothness relative to plain PID. Benchtop validation confirms the efficacy of both GMR-generated and symmetric-generated trajectories. Furthermore, experimental results show a reduction of 40 N in peak interaction force (from 120 N to 80 N), resulting in less mechanical strain on the user. By unifying phase-consistent gait synthesis with passivity shaping, this work advances ankle exoskeleton assistance that is individualized, robust, and inherently safe.
51
Wednesday June 3, 2026
Oral Presentation
Empirical Prediction of Pedestrian Comfort in Mobile Robot–Pedestrian Encounters
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, by developing collision avoidance algorithms, but not enough studies take the pedestrian's subjective safety or comfort into consideration. Quantifying comfort is a major challenge that hinders mobile robots from understanding and responding to human emotions. We empirically look into the relationship between the mobile robot-pedestrian interaction kinematics and subjective comfort. We perform one-on-one experimental trials, each involving a mobile robot and a volunteer. Statistical analysis of pedestrians' reported comfort versus the kinematic variables shows moderate but significant correlations for most variables. We use the findings and empirically design three comfort estimators/predictors based on the minimum distance, the minimum projected time-to-collision, and a composite estimator. The composite estimator employs all studied kinematic variables and reaches the highest prediction rate and classifying performance among the predictors. The composite predictor has an odds ratio of 3.67. In simple terms, when it identifies a pedestrian as comfortable, it is almost 4 times more likely that the pedestrian is comfortable rather than uncomfortable. The study provides a comfort quantifier for incorporating pedestrian feelings into path planners for more socially compliant robots.
52
Wednesday June 3, 2026
Oral Presentation
Estimating Human Muscular Fatigue in Dynamic Collaborative Robotic Tasks with Learning-Based Models
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Assessing human muscle fatigue is critical for optimizing performance in physical human–robot interaction (pHRI) tasks and mitigating safety risks for the human operator. This paper presents a data-driven framework for estimating muscle fatigue in dynamic pHRI tasks using surface electromyography (sEMG) sensors attached to the human arm. Subject-specific machine learning (ML) regression models were developed to estimate fatigue during cyclic (i.e., repetitive) pHRI tasks. Machine learning models were trained to estimate the fraction of cycles to fatigue (FCF) using EMG features. Their performance was compared with a CNN model that processes spectrogram representations of EMG signals. Unlike most earlier data-driven approaches that primarily formulated fatigue estimation as a classification problem, our method models the continuous progression of fatigue through regression, enabling tracking of gradual physiological changes rather than discrete states, which is critical for timely intervention and adaptive control in dynamic pHRI tasks. Experiments were conducted with ten participants who interacted with a collaborative robot operated under an admittance controller, performing lateral (left-right) cyclic movements of the end effector until the onset of muscular fatigue. The results demonstrate that the root mean square error (RMSE) of FCF estimation across participants was 20.8 ± 4.3%, 23.3 ± 3.8%, 24.8 ± 4.5%, and 26.9 ± 6.1% for the CNN, Random Forest, XGBoost, and Linear Regression models, respectively. To examine cross-task generalization, additional experiments were performed with one participant who executed vertical and circular repetitive movements. Models trained solely on the lateral-movement data were directly tested on these unseen tasks. The results indicate that the proposed models are robust to variations in movement direction, arm kinematics, and muscle recruitment patterns, while the Linear Regression model performed poorly.
53
Wednesday June 3, 2026
Oral Presentation
Look at Them Go! Using an Autonomous Assistive GoBot to Encourage Movement Practice by Two Children with Motor Disabilities
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Young children with motor disabilities face barriers and delays to learning motor skills such as walking. Pediatric body-weight support harness systems (BWSHes) are a newer technology for helping young children to practice supported motor skills. Incorporating an assistive robot to mediate BWSH interventions can support further child motion and engagement, but almost no work to date has studied autonomous robot-mediated BWSH use. We conducted a six-month-long single-case study series with two participants to evaluate the effectiveness of an autonomous assistive robot in motivating the children to move and stay engaged while in the BWSH. We collected and analyzed objective movement data and self-reported parent survey data to determine how much the child moved and stayed engaged during sessions. Our results showed that both children displayed more movement while the assistive robot was active (relative to in prior no-robot periods). Parents also rated their children as more engaged while the assistive robot was present. An autonomous assistive robot may provide motivation for a child to move and stay engaged while using a pediatric rehabilitation aid such as a BWSH. The products of this work can benefit roboticists who work with children with disabilities and researchers who use pediatric rehabilitation technologies.
54
Thursday June 4, 2026
Oral Presentation
A Multi-Layer Sim-To-Real Framework for Gaze-Driven Assistive Neck Exoskeletons
Location: Regular Session, Hall A2
Time: 17:35-17:45
Abstract: Dropped head syndrome, caused by neck muscle weakness from neurological diseases, severely impairs an individual’s ability to support and move their head, causing pain and making everyday tasks challenging. Our long-term goal is to develop an assistive powered neck exoskeleton that restores natural movement. However, predicting a user’s intended head movement remains a key challenge. We leverage virtual reality (VR) to collect coupled eye and head movement data from healthy individuals to train models capable of predicting head movement based solely on eye gaze. We also propose a novel multi-layer controller selection framework, where head control strategies are evaluated across decreasing levels of abstraction—from simulation and VR to a physical neck exoskeleton. This pipeline effectively rejects poor-performing controllers early, identifying two novel gaze-driven models that achieve strong performance when deployed on the physical exoskeleton. Our results reveal that no single controller is universally preferred, highlighting the necessity for personalization in gaze-driven assistive control. Our work demonstrates the utility of VR-based evaluation for accelerating the development of intuitive, safe, and personalized assistive robots.
55
Thursday June 4, 2026
Oral Presentation
Energy-Based Auto-Tuning of Velocity Flow Controller for Exoskeleton-User Speed Synchronization
Location: Regular Session, Hall A2
Time: 17:35-17:45
Abstract: The Velocity Flow Field (VFF) lower-limb exoskeleton controller is widely applicable for gait rehabilitation because it provides the user with considerable agency over their gait; however, previous studies reported the feeling of "walking through water", and resistance to the user's efforts. In this work, a mathematical explanation for the viscous damping behavior when users deviate from the reference trajectory is presented. The controller was corrected and an adaptation law is proposed that synchronizes the speed gain with the user's current walking speed by minimizing the average mechanical work transferred between the user and exoskeleton per step. Experiments comparing a fixed and adaptive controller with 12 participants walking at 0.4 +/- 0.1 body length/s on a treadmill showed that the adaptive controller tracks changes in walking speed, while reducing the energy absorbed by 0.589 +/- 0.126 J/step compared to the fixed controller at the fastest walking speed. Analysis of changes in muscle effort and interaction torques with a human-exoskeleton interaction portrait showed that for most participants, the adaptive controller at medium and fast speeds substantially reduced user-controller disagreement and increased user agency over the walking motion. These positive results suggest that optimizing the energy supplied per step can serve as an effective coordination mechanism, enabling personalized and real-time adjustments of walking speed between the user and the exoskeleton.
56
Thursday June 4, 2026
Oral Presentation
Tactile-Based Human Intent Recognition for Robot Assistive Navigation
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Robot assistive navigation (RAN) is critical for enhancing the mobility and independence of the growing population of mobility-impaired individuals. However, existing systems often rely on interfaces that fail to replicate the intuitive and efficient physical communication observed between a person and a human caregiver, limiting their effectiveness. In this paper, we introduce Tac-Nav, a RAN system that leverages a cylindrical tactile skin mounted on a Stretch 3 mobile manipulator to provide a more natural and efficient interface for human navigational intent recognition. To robustly classify the tactile data, we developed the Cylindrical Kernel Support Vector Machine (CK-SVM), an algorithm that explicitly models the sensor's cylindrical geometry and is consequently robust to the natural rotational shifts present in a user's grasp. Comprehensive experiments were conducted to demonstrate the effectiveness of our classification algorithm and the overall system. Results show that CK-SVM achieved superior classification accuracy on both simulated (97.1%) and real-world (90.8%) datasets compared to four baseline models. Furthermore, a pilot study confirmed that users more preferred the Tac-Nav tactile interface over conventional joystick and voice-based controls. Code and video are available at: https://sites.google.com/view/tac-nav/home.
57
Thursday June 4, 2026
Oral Presentation
Fabric Pneumatic Artificial Muscle-Based Head-Neck Exosuit: Design, Evaluation, and Modeling
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Wearable exosuits assist human movement in tasks ranging from rehabilitation to daily activities; specifically, head-neck support is necessary for patients with certain neurological disorders. Rigid-link exoskeletons have shown to enable head-neck mobility compared to static braces, but their bulkiness and restrictive structure inspire designs using ``soft" actuation methods. In this paper, we propose a fabric pneumatic artificial muscle-based exosuit design for head-neck support. We describe the design of our prototype and physics-based model, enabling us to derive actuator pressures required to compensate for gravitational load. Our modeled range of motion and workspace analysis indicate that the limited actuator lengths impose slight limitations (83% workspace coverage), and gravity compensation imposes a more significant limitation (43% workspace coverage). We introduce compression force along the neck as a novel, potentially comfort-related metric. We further apply our model to compare the torque output of various actuator placement configurations, allowing us to select a design with stability in lateral deviation and high axial rotation torques. The model correctly predicts trends in measured data where wrapping the actuators around the neck is not a significant factor. Our test dummy and human user demonstration confirm that the exosuit can provide functional head support and trajectory tracking, underscoring the potential of artificial muscle–based soft actuation for head–neck mobility assistance.
58
Thursday June 4, 2026
Oral Presentation
Robust Friction Estimation for an Active Upper-Limb Exoskeleton Via SOSML Observer
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Friction in compact, geared actuators remains a primary barrier to transparency in upper-limb exoskeletons, especially near zero velocity and during frequent reversals. A momentum-based estimation framework is developed and evaluated on a two-DoF active device (modified EDUExo), where joint friction is recovered from on-board joint measurements and fitted to Coulomb–viscous and Stribeck laws. Two estimators are compared under identical conditions: a first-order momentum observer (FO) and a second-order sliding-mode momentum observer (SOSML). Three velocity trajectories are designed to probe complementary behaviors. In simulation, SOSML adheres more closely to the S-shaped friction law, and preserves loop symmetry under encoder noise; parameter variance and robustness under structured model mismatch are likewise improved relative to FO. The results indicate that SOSML delivers lower lag, cleaner noise profiles, and reduced parameter drift without changing the signal set or adding sensors, thereby strengthening friction identification and compensation on compact, gear-reduced actuators.
59
Thursday June 4, 2026
Oral Presentation
WATCHDOG: Autonomous Elderly Assistance Via Attention-Based Fall Detection and Trajectory Prediction
Location: Interactive Session, Hall C
Time: 15:00-16:30
Abstract: Service robots designed to assist elderly people are receiving significant attention since they can improve their quality of life, promote their independence, and provide daily support. These mobile platforms can observe people moving around their homes, recognise dangerous events, and detect them promptly. This paper introduces a novel framework to perform fall detection and people following on board an autonomous legged robotic platform. The system operates on the Unitree Go2 robot and comprises two main building blocks. The first component consists of a Body Landmarks extractor and a Transformer-based network that performs binary classification, distinguishing between Fall behaviours and Activities of Daily Living (ADL). The second component is a target-driven path planner that enables the robot to follow and maintain a full-body view of the target in complex environments. Experiments on public datasets and comparison with state-of-the-art works have been conducted to demonstrate the reliability of both blocks. Real experiments in a cluttered environment have been performed to illustrate how the mobile platform is able to follow people moving around obstacles, detect falls in occluded areas, and predict people’s trajectories to maintain a full-body view.
60
Thursday June 4, 2026
Oral Presentation
A Novel Upper Limb Rehabilitation Framework Based on Dual-Arm Robotics for Therapist-Like Traction Training
Location: Regular Session, Hall A2
Time: 11:00-11:10
Abstract: In this letter, we propose a novel upper limb rehabilitation framework based on dual-arm robotics for therapist-like traction training. Prioritizing patient safety, an 8-DOF kinematic model of the upper limb complex is derived to evaluate the reachable workspace of the end-of-arm and forearm during interaction with a dual-arm robot. Leveraging the characteristics of dual-arm rehabilitation, a non-redundant inverse kinematics method is proposed to constrain joint angles, thereby establishing a safety mechanism under dual constraints. Secondly, considering the training science and compliance, a potential field control strategy is introduced to enable the robot to learn the therapist's traction characteristics from a single demonstration. Combined with the master-slave control, it reproduces the therapist's assistance and allows for compliant interaction. Experimental results show that the proposed framework combines the strong adaptability and comfort of end-effector robots with the precise rehabilitation of exoskeleton robots. As dual-arm and humanoid robots become more widely adopted, the proposed scheme holds promise for delivering therapist-like safe, scientific, and compliant rehabilitation in clinical and home settings.
61
Thursday June 4, 2026
Oral Presentation
Development of a Mixed-Control Ankle Assist Device with Sensor-Fusion-Based Phase Recognition for Walking Exercise Promotion
Location: Regular Session, Hall A2
Time: 11:00-11:10
Abstract: "Frail" elderly often experience walking impairments that limit independence and sustained physical activity. Although various assistive devices exist, many rely on single-mode control, limiting adaptability, responsiveness to gait variability, and voluntary motion. To improve, we developed a wearable ankle-assist device with real-time gait phase recognition and multi-mode control. Sensor fusion of inertial and plantar-pressure data enables robust five-phase segmentation, with optimal weights tuned by Particle Swarm Optimization. Based on detected gait phase, the controller dynamically switches between speed, torque, and free modes, adapting to cadence variations. Treadmill experiments showed that mixed control increased walking distance (251 m to 282 m (p < 0.05)), reduced heart rate change (20% to 10% (p < 0.01)). Gait analysis confirmed comfort and less resistance. These findings demonstrate that phase-aware adaptive assistance balances propulsion and natural motion, supporting mobility and reducing strain. This framework provides a practical basis for wearable ankle-assist systems in elderly rehabilitation and daily use.
62
Thursday June 4, 2026
Oral Presentation
Reinforcement Learning Control Outperforms Iterative Learning in Exoskeleton-Assisted Gait Training
Location: Regular Session, Hall A2
Time: 11:00-11:10
Abstract: Learning-based controllers are increasingly adopted in lower-extremity powered exoskeletons, yet their advantages over traditional adaptive approaches remain underexplored. We compared two adaptive assist-as-needed (AAN) controllers for gait training with an ankle exoskeleton: a reinforcement learning-based controller (RL-AAN) and a conventional iterative learning controller (ILC-AAN). Both adjusted assistance stride-by-stride, delivering torque as a percentage of the wearer's biological plantarflexion moment—estimated online with a subject-agnostic model—and progressively faded assistance as performance improved. Healthy participants walked on a self-paced treadmill under a perturbed-gait protocol. Performance was assessed as average percent stride-velocity (SV) improvement relative to unassisted perturbed walking (Δ%εSV+) and percent of strides above the SV threshold (N%SV+). During training, RL-AAN and ILC-AAN elicited comparable gains in Δ%εSV+ between the first and last training sessions, but RL-AAN yielded greater adherence across sessions, as indicated by larger N%SV+. After training, RL-AAN demonstrated superior retention in Δ%εSV+ and N%SV+. These results support RL-AAN as a promising strategy for subject-tailored gait training, motivating future studies in neurological and musculoskeletal populations.
63
Thursday June 4, 2026
Oral Presentation
Learning a Shape-Adaptive Assist-As-Needed Rehabilitation Policy from Therapist-Informed Input
Location: Regular Session, Hall A2
Time: 11:00-11:10
Abstract: Therapist-in-the-loop robotic rehabilitation has shown the promise to enhance rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, its broader adoption is limited due to insufficient interaction and limited adaptation capability. This article proposes a novel telerobotics-mediated framework that enables therapists to deliver assist-as-needed~(AAN) therapy based on two primary contributions. First, the reference motion for movement therapy is generated to encourage the active participant of patients based on their motion preferences encoded using a probabilistic model. Second, the telerobotics-mediated system enable the therapist to inform the via-points, enabling minimal but effective assistance for AAN therapy by partially deforming the reference motion. The effectiveness of the proposed strategy was validated a telerobotic system through two representative rehabilitation tasks, demonstrating its potential for remote AAN therapy.
64
Thursday June 4, 2026
Oral Presentation
Enabling Automated and Personalized Motor Assessment in Neurorehabilitation: Generating Patient-Specific Reference Movements with a Virtual Humanoid Twin
Location: Regular Session, Hall A2
Time: 11:00-11:10
Abstract: Recovering upper-limb motor functions impaired by trauma or neurological disease is a long and challenging process. To monitor a patient’s progress through the various stages of rehabilitation and guide therapy, regular movement assessment is essential. However, such evaluations are rarely conducted in clinical practice due to time constraints and the need for cumbersome equipment. A key limitation is the access to reference motion data, typically derived from averaged movements of unimpaired individuals, which requires new data collection for each task and lacks personalization (e.g., accounting for individual morphology or motor abilities). We present a novel method to generate patient-specific reference motions directly from the patient’s hand pose using a personalized model of the patient, the Virtual Humanoid Twin (VHT). By solving an ergonomic-based optimal control problem, our approach produces tailored reference motions without prior task-specific data. We validated this method on two motor tasks (reaching and pouring) using data from seven unimpaired participants, with and without an elbow orthosis restricting motion. Analysis of joint trajectories, range of motion, and normalized multi-dimensional Dynamic Time Warping confirmed that VHT-generated motions were more ergonomic than those with the orthosis and closely matched natural movements. The method’s rapid generation time can also enable real-time reference motion estimation, parallel to the patient’s movements. This innovation simplifies access to reference motions while providing personalization. It creates opportunities for automated motor assessment in neurorehabilitation, enhancing patient recovery tracking through regular evaluations.
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Thursday June 4, 2026
Oral Presentation
A Framework for Adaptive Load Redistribution in Human-Exoskeleton-Cobot Systems
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Wearable devices like exoskeletons are designed to reduce excessive loads on specific joints of the body. Specifically, single- or two-degrees-of-freedom (DOF) upper-body industrial exoskeletons typically focus on compensating for the strain on the elbow and shoulder joints. However, during daily activities, there is no assurance that external loads are correctly aligned with the supported joints. Optimizing work processes to ensure that external loads are primarily (to the extent that they can be compensated by the exoskeleton) directed onto the supported joints can significantly enhance the overall usability of these devices and the ergonomics of their users. Collaborative robots (cobots) can play a role in this optimization, complementing the collaborative aspects of human work. In this study, we propose an adaptive and coordinated control system for the human-cobot-exoskeleton interaction. This system adjusts the task coordinates to maximize the utilization of the supported joints. When the torque limits of the exoskeleton are exceeded, the framework continuously adapts the task frame, redistributing excessive loads to non-supported body joints to prevent overloading the supported ones. We validated our approach in an equivalent industrial painting task involving a single-DOF elbow exoskeleton, a cobot, and four subjects, each tested in four different initial arm configurations with five distinct optimisation weight matrices and two different payloads.
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Thursday June 4, 2026
Oral Presentation
Real-Time Decoding of Movement Onset and Offset for Brain-Controlled Rehabilitation Exoskeleton
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Robot-assisted therapy can deliver high-dose, task-specific training after neurologic injury, but most systems act primarily at the limb level - engaging the impaired neural circuits only indirectly - which remains a key barrier to truly contingent, neuroplasticity-targeted rehabilitation. We address this gap by implementing online, dual-state motor imagery control of an upper-limb exoskeleton, enabling goal-directed reaches to be both initiated and terminated directly from noninvasive EEG. Eight participants used EEG to initiate assistance and then volitionally halt the robot mid-trajectory. Across two online sessions, group-mean hit rates were 61.5% for onset and 64.5% for offset, demonstrating reliable start–stop command delivery despite instrumental noise and passive arm motion. Methodologically, we reveal a systematic, class-driven bias induced by common task-based recentering using an asymmetric margin diagnostic, and we introduce a class-agnostic fixation-based recentering method that tracks drift without sampling command classes while preserving class geometry. This substantially improves threshold-free separability (AUC gains: onset +56%, p=0.0117; offset +34%, p=0.0251) and reduces bias within and across days. Together, these results help bridge offline decoding and practical, intention-driven start–stop control of a rehabilitation exoskeleton, enabling precisely timed, contingent assistance aligned with neuroplasticity goals while supporting future clinical translation.
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Thursday June 4, 2026
Oral Presentation
A Lightweight Hip Exoskeleton with High Torque-To-Mass Ratio: Design, Gait-Synchronized Control, and Physiological Validation
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: This paper presents the design, control, and experimental validation of a lightweight hip exoskeleton for walking assistance. By integrating quasi-direct drive actuators, single-piece stainless steel frames, and passive revolute joints, the device achieves a high torque-to-mass ratio while maintaining a compact and lightweight structure. A delayed output feedback control strategy synchronizes assistive torque with the gait cycle by actively leading the wearer's hip motion, with user studies identifying a consistent optimal phase difference across participants and walking speeds, eliminating repeated calibration. Surface electromyography validates the assistance, demonstrating substantial reductions in activation of the vastus medialis and vastus lateralis at the optimal time delay. Power analysis further confirms that this setting maximizes positive power transfer while minimizing resistive effects. The proposed exoskeleton delivers physiologically meaningful and energetically efficient hip assistance suitable for everyday mobility support.
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Thursday June 4, 2026
Oral Presentation
CareBot-H: Enhancing Patient Transfer with Biomimetic Design and Trajectory Deformation Algorithm
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: This paper introduces the CareBot-H Robot, a humanoid nursing robot designed to perform patient transfer tasks in confined environments. The robot is equipped with biomimetic arms that replicate human arm size and function, and distributed tactile sensors that enhance operational safety during physical contact. To achieve stable and anthropomorphic motion, a trajectory deformation algorithm is proposed. The method comprises an offline phase, where expert demonstrations are encoded into prior trajectories using a Variational Autoencoder (VAE), and an online phase, where a tactile-informed Zero-Moment Point (ZMP) model enables real-time trajectory adjustment. Experimental validation with human participants demonstrates that the proposed approach outperforms manual teleoperation, producing smoother and more efficient transfer trajectories while significantly reducing deviations between actual and ideal ZMP. These results indicate that the CareBot-H achieves reliable and safe patient transfer performance, offering practical potential for deployment in real-world nursing care scenarios.
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Thursday June 4, 2026
Oral Presentation
Innovative Design of Multi-Functional Supernumerary Robotic Limbs with Ellipsoid Workspace Optimization
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Supernumerary robotic limbs (SRL) offer substantial potential in both the rehabilitation of hemiplegic patients and the enhancement of functional capabilities for healthy individuals. Designing a general-purpose SRL device is inherently challenging, particularly when<p>developing a unified theoretical framework that meets the diverse functional requirements of both upper and lower limbs. In this paper, we propose a MOO design theory that integrates grasping workspace similarity, walking workspace similarity, bracing force for STS movements, and overall mass and inertia. To facilitate rapid and stable<p>convergence of the model to high-dimensional irregular Pareto fronts, we introduce a multi-subpopulation correction firefly algorithm. The optimized solution is utilized to redesign the prototype for experimentation to meet specified requirements. Six healthy participants and two hemiplegia patients participated in real experiments. Compared to the pre-optimization results, the average grasp success rate improved by 7.2%, while muscle activity during walking and STS tasks decreased by an average of 12.7% and 25.1%, respectively, following the optimization.
"
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Thursday June 4, 2026
Oral Presentation
A Wearable Isokinetic Training Robot for Enhanced Bedside Knee Rehabilitation
Location: Interactive Session, Hall C
Time: 09:00-10:30
Abstract: Knee pain is prevalent in over 20% of the population, limiting the mobility of those affected. In turn, isokinetic dynamometers and robots have been used to facilitate rehabilitation for those still capable of ambulation. However, there are at most only a few wearable robots capable of delivering isokinetic training for bedridden patients. Here, we developed a wearable robot that provides bedside isokinetic training by utilizing a variable stiffness actuator and dynamic energy regeneration. The efficacy of this device was validated in a study involving six subjects with debilitating knee injuries. During two courses of rehabilitation over a total of three weeks, the average peak torque, average torque, and average work produced by their affected knees increased significantly by 81.0%, 101.4%, and 117.6%, respectively. Furthermore, the device’s energy regeneration features were found capable of extending its operating time to 198 days under normal usage, representing a 57.8% increase over the same device without regeneration. These results suggest potential methodologies for delivering isokinetic joint rehabilitation to bedridden patients in areas with limited infrastructure.
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Friday, 5 June 2026
Workshop
Tailored to Move: Wearable Robotics for Motion Assistance
Time: FULL DAY
Website: https://sites.google.com/view/tailored-to-move-icra2026/home
Abstract: Wearable robotics is a rapidly evolving domain at the intersection of robotics, materials, biomechanics, human-computer interaction, and clinical practice, with significant implications for healthcare, rehabilitation, aging, and human augmentation. The proposed workshop directly addresses this nascent multidisciplinary landscape by focusing on wearable devices’ user-centered design, comfort, sensing, and personalized actuation: critical factors that determine real-world success and adoption of wearable robotic systems.
The relevance to ICRA 2026 lies in its focus on emerging challenges beyond actuation and control. The workshop highlights the human-in-the-loop perspective, the integration of truly wearable sensors and actuators, and the need for context-aware, symbiotic and adaptive assistance: areas increasingly aligned with cutting-edge work in robotics, AI, and human-robot collaboration. The workshop also engages with regulatory and ethical developments, including the EU AI Act, which will soon shape how robotic systems are designed and deployed in human-facing applications. Additional aspects important in the design of wearable devices are also addressed, including smart textile and energy systems, as are the effects of wearable use in the physical and mental health of their users, and the importance of such devices in rehabilitation and regaining independence for older adults.
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Tuesday, 2 June
Keynote
Towards Wearable Robotics with better Portability, Safety, and Comfort
Location: Hall A1 (Plenary)
Time: 16:45 – 18:15
Speakers: Haoyong Yu, National University of Singapore (NUS)
Abstract:With the rapid population aging in many developed countries, wearable robotics, commonly known as exoskeleton robots, are believed to have wide applications in both industry and Healthcare. However, the current market size of wearable robotics is still quite small compared with other robotics sectors due to the limitations in portability, safety, and user comfort.
At NUS Biorobotics Lab, we are developing a series of wearable robotics for rehabilitation and worker assistance. We adopt a modular approach based on a set of core technologies and components developed in the lab, which includes compliant actuation, cable drive mechanism, wearable sensors and learning based movement detection algorithms. We achieved better portability with novel differential mechanism design and cable driven transmission, safer human robot interaction with compliant actuation, and better human robot movement synchronization with novel sensing strategies.
In this talk, I will give a brief introduction to some of our wearable robots in real world applications.
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