Understanding Physical Effects for
Effective Tool-use

2022

1. RA-L

   

   Understanding Physical Effects for Effective Tool-use

   Tool Manipulation Functionality Affordance HOI

   Zeyu Zhang^*, Ziyuan Jiao^*, Weiqi Wang, Yixin Zhu, Song-Chun Zhu, and Hangxin Liu^†

   IEEE Robotics and Automation Letters (RA-L), 2022

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   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.

   @article{zhang2022understanding,
     title = {Understanding Physical Effects for Effective Tool-use},
     author = {Zhang, Zeyu and Jiao, Ziyuan and Wang, Weiqi and Zhu, Yixin and Zhu, Song-Chun and Liu, Hangxin},
     journal = {IEEE Robotics and Automation Letters (RA-L)},
     volume = {7},
     number = {4},
     pages = {9469--9476},
     year = {2022},
     publisher = {IEEE},
   }