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Preprint.pdf

2303.03486.pdf
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🔗 Source: arxiv.org
📊 Size: 3.01 MB
📄 Pages: 11 pages
⬇️ Downloads: 51

Summary

The method combines the strengths of RL and SBP to train motor control policies for in-hand manipulation with finger gaiting, allowing for the manipulation of concave shapes without relying on support surfaces. The approach uses non-holonomic Rapidly-Exploring Random Trees (RRT) algorithms with added constraints to explore the problem state space and generate reset distributions for RL training. The results show that this method enables more efficient training of RL control policies and allows for the demonstration of dexterous skills not previously shown, such as in-hand manipulation of concave shapes with only intrinsic sensing and no support surfaces.

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Description

The method combines the strengths of RL and SBP to train motor control policies for in-hand manipulation with finger gaiting, allowing for the manipulation of...

Technical Information

  • File Format: PDF
  • File Size: 3.01 MB
  • Pages: 11
  • Language: EN
  • Total Downloads: 51
  • Last Updated: 8 hours ago

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