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arXiv · 2010.15335

Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions

Constantinos Chamzas·Zachary Kingston·Carlos Quintero-Peña·Anshumali Shrivastava·Lydia E. Kavraki
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Abstract

Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME , two experience-based frameworks for sampling-based planning applicable to complex manipulators in 3 D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.

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Constantinos Chamzas, Zachary Kingston, Carlos Quintero-Peña, Anshumali Shrivastava, Lydia E. Kavraki. 2021-10-15. Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions. https://doi.org/10.1109/icra48506.2021.9561104

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