Know Thyself: Transferable Visual Control Policies Through Robot-Awareness

Understand

Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data.

• How might 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 control" paradigm that achieves this by exploiting readily available knowledge about the robot.
• We then instantiate this in a robot-aware model-based RL policy by training modular dynamics models that couple a transferable, robot-aware world dynamics module with a robot-specific, potentially analytical, robot dynamics module.
• This also enables us to set up visual planning costs that separately consider the robot agent and the world.