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REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
Xingyu Liu · Deepak Pathak · Kris Kitani

Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Room 307

Popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but often impractical for complex robots. In this work, we consider the problem of transfer policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being different in different robots. In this paper, we propose a novel method of using continuous evolutionary models for robotic policy transfer. We interpolate between the source robot and the target robot by finding a continuous evolutionary change of robot parameters. An expert policy on the source robot is transferred through iteratively finetuning on the intermediate robots that gradually evolve to the target robot. Experiments show that the proposed continuous evolutionary model can effectively transfer the policy across robots and achieve superior sample efficiency on new robots. The proposed method is especially advantageous in sparse reward settings where exploration can be significantly reduced.

Author Information

Xingyu Liu (Carnegie Mellon University)
Deepak Pathak (Carnegie Mellon University)
Kris Kitani (Carnegie Mellon University)

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