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Policy Stitching: Learning Transferable Robot Policies
Training robots with reinforcement learning (RL) typically involves heavy
interactions with the environment, and the acquired skills are often sensitive
to changes in task environments and robot kinematics. Transfer RL aims to
leverage previous knowledge to accelerate learning of new tasks or new body
configurations. However, existing methods struggle to generalize to novel
robot-task combinations and scale to realistic tasks due to complex
architecture design or strong regularization that limits the capacity of the
learned policy. We propose Policy Stitching, a novel framework that facilitates
robot transfer learning for novel combinations of robots and tasks. Our key
idea is to apply modular policy design and align the latent representations
between the modular interfaces. Our method allows direct stitching of the robot
and task modules trained separately to form a new policy for fast adaptation.
Our simulated and real-world experiments on various 3D manipulation tasks
demonstrate the superior zero-shot and few-shot transfer learning performances
of our method. Our project website is at:
http://generalroboticslab.com/PolicyStitching/ .Comment: CoRL 202
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