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ECE 6504: Advanced Topics in Robotic Decision Making
All papers and notes posted on canvas
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Instructor: Pratap Tokekar
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Mon/Wed 4PM - 5:15PM
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DateLecturePrimary Reading/Paper 1Further Reading/Paper 2SubmissionsImportant Dates
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Mon, Aug 28
Introduction + Secretary Hiring Problem
Who solved the secretary problem? Ferguson
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Wed, Aug 30More Secretary Hiring ProblemCardinal payoff - Bearden
Multiple choice secretary - Kleinberg
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Mon, Sep 4No class - Labor day
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Wed, Sep 6SubmodularitySubmodular function maximization - Krause and Golovin
L. Lovasz. Submodular functions and convexity. Mathematical programming: the state of the
art, Bonn, 235–257, 1982.
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Mon, Sep 11Submodularity + MatroidsSubmodular function maximization - Krause and Golovin
http://jeffe.cs.illinois.edu/teaching/algorithms/notes/08-matroids.pdf
https://theory.stanford.edu/~jvondrak/CS369P/lec8.pdf
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Wed, Sep 13No class - ICRA
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Mon, Sep 18
Sensor Placement (Mutual Information + Gaussian Processes)
Near-Optimal Sensor Placements in Gaussian Processes. Guestrin et al. ICML 2005Elements of Information Theory. Cover and Thomas.
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Wed, Sep 20
Sensor Placement (Mutual Information + Gaussian Processes)
Near-Optimal Sensor Placements in Gaussian Processes. Guestrin et al. ICML 2005
Elements of Information Theory. Cover and Thomas.
Gaussian Processes for Machine Learning. Rasmussen and Williams.
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Mon, Sep 25
Sensor Placement (Mutual Information + Gaussian Processes)
Near-Optimal Sensor Placements in Gaussian Processes. Guestrin et al. ICML 2005
Elements of Information Theory. Cover and Thomas.
Gaussian Processes for Machine Learning. Rasmussen and Williams.
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Wed, Sep 27
Sensor Placement (Mutual Information + Gaussian Processes)
Near-Optimal Sensor Placements in Gaussian Processes. Guestrin et al. ICML 2005
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Mon, Oct 2Submodular Orienteeringhttp://chekuri.cs.illinois.edu/papers/orienteering.pdf
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Wed, Oct 4Multi-Armed Bandit Problems
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2-3), 235-256.
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Mon, Oct 9
Multi-Armed Bandit Problems + Gaussian Processes
Finite-time analysis of the multiarmed bandit problem. Auer et al. Machine learning, 2002.
Gaussian process optimization in the bandit setting: No regret and experimental design. Srinivas et al., ICML 2010
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Wed, Oct 11Distributed Voronoi Coverage
Coverage control for mobile sensing networks. Cortes et al. IEEE Transactions on robotics and Automation, 2004.
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Mon, Oct 16Chance-constrained optimization
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Wed, Oct 18MDP + POMDP
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Mon, Oct 23RL
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WedOct25
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MonOct30
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WedNov1Varun
Ling, C. K., Low, K. H., & Jaillet, P. (2016, February). Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond. In AAAI (pp. 1860-1866).
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MonNov6Jun & Pratik
Lawrance, N. R., Chung, J. J., & Hollinger, G. A. (2017). Fast Marching Adaptive Sampling. IEEE Robotics and Automation Letters, 2(2), 696-703.
Williams, Ryan K., Andrea Gasparri, and Giovanni Ulivi. "Decentralized matroid optimization for topology constraints in multi-robot allocation problems." Robotics and Automation (ICRA), 2017 IEEE International Conference on. IEEE, 2017.
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WedNov8Yoon & Murtaza
Gharesifard, B., & Smith, S. L. (2017). Distributed Submodular Maximization with Limited Information. IEEE Transactions on Control of Network Systems.
Segui-Gasco, P., Shin, H. S., Tsourdos, A., & Seguí, V. J. (2015, September). Decentralised submodular multi-robot task allocation. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 2829-2834). IEEE.
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MonNov13Project Updates & Javier
Yang, Shiyi, et al. "Real-time optimal path planning and wind estimation using Gaussian process regression for precision airdrop." American Control Conference (ACC), 2017. IEEE, 2017.
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WedNov15Lifeng & Yingqiu
Yang, F., & Chakraborty, N. (2017, May). Algorithm for optimal chance constrained linear assignment. In Robotics and Automation (ICRA), 2017 IEEE International Conference on(pp. 801-808). IEEE.
Vitus, M. P., Zhou, Z., & Tomlin, C. J. (2016). Stochastic Control With Uncertain Parameters via Chance Constrained Control. IEEE Transactions on Automatic Control , 61 (10),2892-2905. http://ieeexplore.ieee.org/abstract/document/7364185/
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MonNov20No class - Thanksgiving break
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WedNov22No class - Thanksgiving break
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MonNov27Kevin & Zhongshun
Lacerda, B., Parker, D., & Hawes, N. (2017). Multi-Objective Policy Generation for Mobile Robots Under Probabilistic Time-Bounded Guarantees.
Azar, M. G., Osband, I., & Munos, R. (2017). Minimax Regret Bounds for Reinforcement Learning. arXiv preprint arXiv:1703.05449.
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WedNov29Maymoonah & Naresh
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866.
Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017). Robust Adversarial Reinforcement Learning. arXiv preprint arXiv:1703.02702.
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MonDec4Barnabas & Badour
Finn, C., & Levine, S. (2017, May). Deep visual foresight for planning robot motion. In Robotics and Automation (ICRA), 2017 IEEE International Conference on (pp. 2786-2793). IEEE.
Walker, J., Marino, K., Gupta, A., & Hebert, M. (2017). The Pose Knows: Video Forecasting by Generating Pose Futures. arXiv preprint arXiv:1705.00053.
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WedDec6
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MonDec11Project Presentations
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12/13/2017Project Presentations
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