Rethinking Motion Generation for Robots Operating in Human Workspaces

This project develops a new motion planning paradigm for enabling robots to work in the presence of humans or cooperatively with humans as co-workers. The paradigm is intended to be fast, reactive, and responsive to the requirements that arise from human-robot interaction. It involves the definition and subsequent implementation of constraints that encode properties of human-aware paths and can be translated to cost functions characterizing path quality. New motion planners are proposed. The operation of these planners is guided by constraints and their corresponding cost functions. The planners produce paths compatible with a given set of constraints. A mechanism to incorporate user feedback on the relative importance of constraints is provided and semi-autonomous operation of the robots is considered. Importantly, the planners are embedded in a novel hybrid-systems framework that allows a robot to automatically switch among planners, and hence behaviors, in order to take into account different constraints and user preferences that arise in the context of semi-autonomous operation. Besides the actual implementation of the planners on specific platforms, this project also disseminates all developed libraries and planners. Compatibility will the Robot Operating System (ROS) is provided for wide adoption, while tutorials at major conferences are planned. The training of graduate students and female undergraduate students are a central focus of this project.

This work has been supported by grant NSF NRI 1317849.

Related Publications

  1. Y. Wang, A. A. R. Newaz, J. D. Hernández, S. Chaudhuri, and L. E. Kavraki, “Online Partial Conditional Plan Synthesis for POMDPs With Safe-Reachability Objectives: Methods and Experiments,” IEEE Transactions on Automation Science and Engineering, vol. 18, pp. 932–945, Jul. 2021.
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  2. S. D. Butler, M. Moll, and L. E. Kavraki, “A General Algorithm for Time-Optimal Trajectory Generation Subject to Minimum and Maximum Constraints,” in Proceedings of Algorithmic Foundations of Robotics XII, 2020, vol. 13, pp. 368–383.
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  3. R. Luna, M. Moll, J. M. Badger, and L. E. Kavraki, “A Scalable Motion Planner for High-Dimensional Kinematic Systems,” International Journal of Robotics Research, vol. 39, no. 4, pp. 361–388, Apr. 2020.
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  4. J. D. Hernández, M. Moll, and L. E. Kavraki, “Lazy Evaluation of Goal Specifications Guided by Motion Planning,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2019, pp. 944–950.
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  5. E. Vidal Garcia, M. Moll, N. Palomeras, J. D. Hernández, M. Carreras, and L. E. Kavraki, “Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles,” in IEEE International Conference on Robotics and Automation, 2019, pp. 8936–8942. (Top-3 finalist for best student paper award)
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  6. Y. Wang, S. Chaudhuri, and L. E. Kavraki, “Point-Based Policy Synthesis for POMDPs with Boolean and Quantitative Objectives,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1860–1867, Apr. 2019.
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  7. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 285–292, Apr. 2019.
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  8. Z. Kingston, M. Moll, and L. E. Kavraki, “Exploring Implicit Spaces for Constrained Sampling-Based Planning,” International Journal of Robotics Research, vol. 38, no. 10-11, pp. 1151–1178, 2019.
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  9. 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, 2019.
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  10. F. Lagriffoul, N. Dantam, C. Garrett, A. Akbari, S. Srivastava, and L. E. Kavraki, “Platform-Independent Benchmarks for Task and Motion Planning,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3765–3772, Oct. 2018.
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  11. N. T. Dantam, S. Chaudhuri, and L. E. Kavraki, “The Task Motion Kit,” Robotics and Automation Magazine, vol. 25, no. 3, pp. 61–70, Sep. 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, 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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  14. Y. Wang, S. Chaudhuri, and L. E. Kavraki, “Online Partial Conditional Plan Synthesis for POMDPs with Safe-Reachability Objectives,” in Workshop on the Algorithmic Foundations of Robotics, 2018.
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  15. Y. Wang, S. Chaudhuri, and L. E. Kavraki, “Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives,” in Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, Stockholm, Sweden, 2018, pp. 238–246.
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  16. N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “An Incremental Constraint-Based Framework for Task and Motion Planning,” International Journal of Robotics Research, vol. 37, no. 10, pp. 1134-1151. (Invited Article), 2018.
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  17. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Reactive Synthesis For Finite Tasks Under Resource Constraints,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, 2017, pp. 5326–5332.
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  18. Z. Kingston, M. Moll, and L. E. Kavraki, “Decoupling Constraints from Sampling-Based Planners,” in Proceedings of the International Symposium of Robotics Research, Puerto Varas, Chile, 2017.
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  19. W. Baker, Z. Kingston, M. Moll, J. Badger, and L. E. Kavraki, “Robonaut 2 and You: Specifying and Executing Complex Operations,” in Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Austin, TX, 2017.
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  20. S. Butler, M. Moll, and L. E. Kavraki, “A General Algorithm for Time-Optimal Trajectory Generation Subject to Minimum and Maximum Constraints,” in Proceedings of the Workshop on the Algorithmic Foundations of Robotics, 2016.
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  21. N. T. Dantam, K. Bøndergaard, M. A. Johansson, T. Furuholm, and L. E. Kavraki, “Unix Philosophy and the Real World: Control Software for Humanoid Robots,” Frontiers in Robotics and Artificial Intelligence, vol. 3, Mar. 2016.
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  22. J. D. Hernández, M. Moll, E. Vidal Garcia, M. Carreras, and L. E. Kavraki, “Planning Feasible and Safe Paths Online for Autonomous Underwater Vehicles in Unknown Environments,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016, pp. 1313–1320.
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  23. N. T. Dantam, Z. K. Kingston, S. Chaudhuri, and L. E. Kavraki, “Incremental Task and Motion Planning: A Constraint-Based Approach,” in Robotics: Science and Systems, 2016.
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  24. M. Lahijanian, M. R. Maly, D. Fried, L. E. Kavraki, H. Kress-Gazit, and M. Y. Vardi, “Iterative Temporal Planning in Uncertain Environments with Partial Satisfaction Guarantees,” IEEE Transactions on Robotics, vol. 32, no. 3, pp. 583–599, 2016.
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  25. Y. Wang, N. T. Dantam, S. Chaudhuri, and L. E. Kavraki, “Task and Motion Policy Synthesis as Liveness Games,” in Proceedings of the International Conference on Automated Planning and Scheduling, 2016, pp. 536–540.
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  26. K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “Towards Manipulation Planning with Temporal Logic Specifications,” in Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, 2015, pp. 346–352.
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  27. M. Lahijanian, S. Almagor, D. Fried, L. E. Kavraki, and M. Y. Vardi, “This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction,” in Proceedings of The Twenty-Ninth AAAI Conference (AAAI-15), Austin, TX, 2015, pp. 3664–3671.
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  28. Z. Kingston, N. Dantam, and L. E. Kavraki, “Kinematically constrained workspace control via linear optimization,” in Proceedings of the IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 2015, pp. 758–764.
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  29. M. Moll, I. A. Şucan, and L. E. Kavraki, “Benchmarking Motion Planning Algorithms: An Extensible Infrastructure for Analysis and Visualization,” IEEE Robotics & Automation Magazine (Special Issue on Replicable and Measurable Robotics Research), vol. 22, no. 3, pp. 96–102, 2015.
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  30. D. K. Grady, M. Moll, and L. E. Kavraki, “Extending the Applicability of POMDP Solutions to Robotic Tasks,” IEEE Transactions on Robotics, vol. 31, no. 4, pp. 948–961, 2015.
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  31. C. Voss, M. Moll, and L. E. Kavraki, “A Heuristic Approach to Finding Diverse Short Paths,” in IEEE International Conference on Robotics and Automation, Seattle, WA, 2015, pp. 4173–4179.
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  32. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Optimal and Efficient Stochastic Motion Planning in Partially-Known Environments,” in Proceedings of The Twenty-Eighth AAAI Conference on Artificial Intelligence, Quebec City, Canada, 2014, pp. 2549–2555.
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  33. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Fast Stochastic Motion Planning with Optimality Guarantees using Local Policy Reconfiguration,” in Proceedings of the IEEE International Conference on Robotics and Automation, Hong Kong, China, 2014, pp. 3013–3019.
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  34. R. Luna, M. Lahijanian, M. Moll, and L. E. Kavraki, “Asymptotically Optimal Stochastic Motion Planning with Temporal Goals,” in Proceedings of the Workshop on the Algorithmic Foundations of Robotics, Istanbul, Turkey, 2014.
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  35. M. Lahijanian, L. E. Kavraki, and M. Y. Vardi, “A Sampling-Based Strategy Planner for Nondeterministic Hybrid Systems,” in Proceedings of the International Conference on Robotics and Automation, Hong Kong, China, 2014, pp. 3005–3012.
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  36. S. Nedunuri, S. Prabhu, M. Moll, S. Chaudhuri, and L. E. Kavraki, “SMT-Based Synthesis of Integrated Task and Motion Plans for Mobile Manipulation,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2014, pp. 655–662.
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