Understanding Physical Effects for Effective Tool-use

• URL: http://arxiv.org/abs/2206.14998v1
• Date: Thu, 30 Jun 2022 03:13:38 GMT
• Title: Understanding Physical Effects for Effective Tool-use
• Authors: Zeyu Zhang, Ziyuan Jiao, Weiqi Wang, Yixin Zhu, Song-Chun Zhu, Hangxin Liu
• Abstract summary: We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts. We use a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events. In simulation, we demonstrate that the proposed framework can produce more effective tool-use strategies, drastically different from the observed ones in two tasks.
• Score: 91.55810923916454
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training. Leveraging a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events, the essential physical properties contributing to the effects are identified through the proposed Iterative Deepening Symbolic Regression (IDSR) algorithm. We further devise an optimal control-based motion planning scheme to integrate robot- and tool-specific kinematics and dynamics to produce an effective trajectory that enacts the learned properties. In simulation, we demonstrate that the proposed framework can produce more effective tool-use strategies, drastically different from the observed ones in two exemplar tasks.

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