Learning to Retrieve Relevant Experiences for Motion Planning
Author:
Affiliation:
1. Rice University,Department of Computer Science,Houston,TX,USA
Funder
NSF
Rice University Funds
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9811522/9811357/09812076.pdf?arnumber=9812076
Reference57 articles.
1. Kernel density estimation based self-learning sampling strategy for motion planning of repetitive tasks
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3. Multi-homotopy class optimal path planning for manipulation with one degree of redundancy
4. Topological Complexity of Motion Planning
5. Learning to plan in high dimensions via neural exploration-exploitation trees;chen;Int'l Conf Learn Repr,0
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1. Efficient Task Planning Using Abstract Skills and Dynamic Road Map Matching;Springer Proceedings in Advanced Robotics;2023
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