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/

提出了一种基于模型的强化学习策略，通过训练可转移机器人感知的世界动力学模型和机器人特定的动力学模型，从而利用以前在其他机器人上收集的数据来减少对机器人特定数据的需求，并在模拟和实际机器人上实现了视觉控制任务上的零泛化

认知自我: 通过机器感知实现可迁移的视觉控制策略