Robot Motion Planning with an Experience Database

Motion planning is the problem of determining how to get the robot from one point to another. Ideally, robots should have past experiences, of their own and others, inform future actions to operate more robustly and improve their performance over time. Motion planning, as it is largely practiced today, focuses on solving one problem at a time and makes limited use of past history. The goal of this project is to transform the way robots plan their motions by learning to exploit similarities between different experiences and by creating strategies that can adapt to wide range of scenarios. The work will create a bridge between the motion planning community and the information retrieval community, potentially transforming both fields. Training opportunities for diverse students will be offered. All developed software is disseminated under an open source license and infrastructure will enable other researchers to use the experience databases and contribute to them. This project provides a two-pronged approach to transform motion planning using an experience database. First, hashing will be used on an environment to fetch roadmaps for similar environments from a database. A roadmap is a graph representing feasible motions for a robot. These fetched roadmaps will be then lazily composed and refined to allow the robot to plan efficiently in the current environment. The use of prior experience will be done in tandem with planning from scratch; the latter, if successful, can provide a path and add to the experience database. The second prong in the planned approach will be to maintain various performance characteristics of a library of motion planning algorithms. These characteristics will be then used to optimize algorithm performance and construct a portfolio of algorithms that is competitive across various problems. The overall framework will be implemented in the cloud.

This work has been supported by grant NSF RI 1718478.

Related Publications

  1. C. Chamzas, C. Quintero-Peña, Z. Kingston, A. Orthey, D. Rakita, M. Gleicher, M. Toussaint, and L. E. Kavraki, “MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 882–889, Apr. 2022.
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  2. S. Sobti, R. Shome, S. Chaudhuri, and L. E. Kavraki, “A Sampling-based Motion Planning Framework for Complex Motor Actions,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021.
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  3. Z. Kingston, C. Chamzas, and L. E. Kavraki, “Using Experience to Improve Constrained Planning on Foliations for Multi-Modal Problems,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021.
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  4. M. Moll, C. Chamzas, Z. Kingston, and L. E. Kavraki, “HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021.
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  5. C. Quintero-Peña, C. Chamzas, V. Unhelkar, and L. E. Kavraki, “Motion Planning via Bayesian Learning in the Dark,” in ICRA 2021: Workshop on Machine Learning for Motion Planning, 2021.
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  6. C. Chamzas, Z. Kingston, C. Quintero-Peña, A. Shrivastava, and L. E. Kavraki, “Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021, pp. 1283–1289. (Top-4 finalist for best paper in Cognitive Robotics)
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  7. E. Pairet, C. Chamzas, Y. R. Petillot, and L. E. Kavraki, “Path Planning for Manipulation Using Experience-Driven Random Trees,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3295–3302, Apr. 2021.
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  8. Z. Kingston, A. M. Wells, M. Moll, and L. E. Kavraki, “Informing Multi-Modal Planning with Synergistic Discrete Leads,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 3199–3205.
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  9. W. C. Lewis II, M. Moll, and L. E. Kavraki, “How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?,” Rice University, Sep. 2019.
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  10. C. Chamzas, A. Shrivastava, and L. E. Kavraki, “Using Local Experiences for Global Motion Planning,” in 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 8606–8612.
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  11. J. D. Hernández, E. Vidal, M. Moll, N. Palomeras, M. Carreras, and L. E. Kavraki, “Online Motion Planning for Unexplored Underwater Environments using Autonomous Underwater Vehicles,” Journal of Field Robotics, vol. 36, no. 2, pp. 370–396, Nov. 2018.
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  12. Z. K. Kingston, M. Moll, and L. E. Kavraki, “Sampling-Based Methods for Motion Planning with Constraints,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, no. 1, pp. 159–185, May 2018.
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  13. Muhayyuddin, M. Moll, L. E. Kavraki, and J. Rosell, “Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 712–719, Apr. 2018.
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