NSF SHF 1514372:
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.

Related Publications

  1. 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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  2. 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, 2018.
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  3. N. T. Dantam, S. Chaudhuri, and L. E. Kavraki, “The Task Motion Kit,” Robotics and Automation Magazine, 2018.
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  4. S. Butler, M. Moll, and L. E. Kavraki, “A General Algorithm for Time-Optimal Trajectory Generation Subject to Minimum and Maximum Constraints,” in Workshop on the Algorithmic Foundations of Robotics, 2016.
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  5. 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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  6. 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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  7. Y. Wang, N. T. Dantam, S. Chaudhuri, and L. E. Kavraki, “Task and Motion Policy Synthesis as Liveness Games,” in International Conference on Automated Planning and Scheduling, 2016, pp. 536–540.
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