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Training visual control policies from scratch on a new robot typicallyrequires generating large amounts of robot-specific data. How might we leveragedata previously collected on another robot to reduce or even completely removethis need for robot-specific data? We propose a "robot-aware control" paradigmthat achieves this by exploiting readily available knowledge about the robot.We then instantiate this in a robot-aware model-based RL policy by trainingmodular dynamics models that couple a transferable, robot-aware world dynamicsmodule with a robot-specific, potentially analytical, robot dynamics module.This also enables us to set up visual planning costs that separately considerthe robot agent and the world. Our experiments on tabletop manipulation taskswith simulated and real robots demonstrate that these plug-in improvementsdramatically boost the transferability of visual model-based RL policies, evenpermitting zero-shot transfer of visual manipulation skills onto new robots.Project website: https://www.seas.upenn.edu/~hued/rac

Know Thyself: Transferable Visual Control Policies Through Robot-Awareness