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Learning Feasibility for Task and Motion Planning in Tabletop Environments.

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Summary
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This study introduces a novel heuristic for task and motion planning (TMP) that leverages learned geometric knowledge. This approach significantly improves planning efficiency and scalability in complex robotic scenarios.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Task and motion planning (TMP) integrates discrete search with continuous motion planning.
  • Integrating continuous geometric information into discrete planners poses significant challenges.
  • Existing TMP methods struggle with scalability in complex, real-world scenarios.

Purpose of the Study:

  • To enhance the scalability of TMP algorithms for fixed robots in tabletop environments.
  • To introduce a robust method for incorporating geometric knowledge into discrete task planners.
  • To reduce the computational cost and improve the efficiency of finding task-motion plans.

Main Methods:

  • Developed a learned classifier to predict feasible motions.
  • Utilized the classifier as a heuristic to guide discrete search in TMP.
  • Employed principled approximations to ensure robustness in diverse and complex scenes.
  • Trained the classifier on minimal exemplar scenes and generalized to complex scenarios.

Main Results:

  • Achieved order-of-magnitude improvements in runtime for diverse tabletop scenarios.
  • Demonstrated significant reduction in the total number of motion planning attempts.
  • Showcased robustness in planning across varied and complex scenes, even with classification errors.
  • The learned heuristic effectively guides the search, minimizing computational penalties.

Conclusions:

  • Combining learned heuristics with planning significantly enhances TMP scalability.
  • The proposed method offers a robust and efficient approach for robotic task and motion planning.
  • This work provides a foundation for more capable and efficient autonomous robotic systems.