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Title: Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
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 3D 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 real and simulated 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.  more » « less
Award ID(s):
1718478
NSF-PAR ID:
10280236
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the International Conference on Robotics and Automation 2021
ISSN:
1050-4729
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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