Know Thyself: Transferable Visuomotor Control Through Robot-Awareness

• URL: http://arxiv.org/abs/2107.09047v1
• Date: Mon, 19 Jul 2021 17:56:04 GMT
• Title: Know Thyself: Transferable Visuomotor Control Through Robot-Awareness
• Authors: Edward S. Hu, Kun Huang, Oleh Rybkin, Dinesh Jayaraman
• Abstract summary: Training visuomotor robot controllers from scratch on a new robot typically requires generating large amounts of robot-specific data. We propose a "robot-aware" solution paradigm that exploits readily available robot "self-knowledge" Our experiments on tabletop manipulation tasks in simulation and on real robots demonstrate that these plug-in improvements dramatically boost the transferability of visuomotor controllers.
• Score: 22.405839096833937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training visuomotor robot controllers from scratch on a new robot typically requires generating large amounts of robot-specific data. Could we leverage data previously collected on another robot to reduce or even completely remove this need for robot-specific data? We propose a "robot-aware" solution paradigm that exploits readily available robot "self-knowledge" such as proprioception, kinematics, and camera calibration to achieve this. First, we learn modular dynamics models that pair a transferable, robot-agnostic world dynamics module with a robot-specific, analytical robot dynamics module. Next, we set up visual planning costs that draw a distinction between the robot self and the world. Our experiments on tabletop manipulation tasks in simulation and on real robots demonstrate that these plug-in improvements dramatically boost the transferability of visuomotor controllers, even permitting zero-shot transfer onto new robots for the very first time. Project website: https://hueds.github.io/rac/

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