A popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but also often impractical for complex robots. In this work, we consider the problem of transferring a 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 mismatched in different robots. In this paper, we propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator. 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 training on a sequence of intermediate robots that gradually evolve into 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 using a physics simulator. The proposed method is especially advantageous in sparse reward settings where exploration can be significantly reduced.

通过在物理模拟器中使用连续进化的模型来实现机器人策略的传递，可以在新机器人上实现优秀的样本利用率，特别是在稀疏奖励的情况下，可以显著减少探索。

REvolveR：机器人间策略转移的连续进化模型