Automating Robot Programming Through Constraint Solving and Motion Planning

The project aims to develop a high-level programming framework, called Robosynth, for personal robots. Here, rather than writing low-level code that defines how a robot must perform a task, the user of the robot writes a specification that defines what is to be accomplished. Given this specification and a model of the robot's environment, Robosynth automatically synthesizes a program that can be executed on the robot. So long as the environment behaves according to the assumed model, all executions of this program are guaranteed to satisfy the user-defined requirements.This approach and its derivatives can make robot programming accessible to a vast untapped body of inexperienced programmers. The technical highlights of the project are the specification language using which users interact with Robosynth, and the algorithms that Robosynth uses for automatic code synthesis. These algorithms simultaneously reason about a logical task level that is concerned with the high-level goals of the robot, as well as a continuous motion level concerned with navigating and manipulating a physical space. At the task level, Robosynth leverages recent methods for analyzing complex systems of logical constraints, for example SMT-solving and symbolic solution of graph games. Motion-level reasoning is performed using sampling-based motion planning techniques.

This work has been supported by grant NSF SHF 1514372.

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, S. Sobti, A. Sciola, M. Moll, and L. E. Kavraki, “Increasing Robot Autonomy via Motion Planning and an Augmented Reality Interface,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1017–1023, Apr. 2020.
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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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.
    pdf publisher details
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  12. 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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  13. 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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  14. 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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