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Motion retargeting between heterogeneous polymorphs with different sizes andkinematic configurations requires a comprehensive knowledge of (inverse)kinematics. Moreover, it is non-trivial to provide a kinematic independentgeneral solution. In this study, we developed a cyclic three-phase optimizationmethod based on deep reinforcement learning for human-robot motion retargeting.The motion retargeting learning is performed using refined data in a latentspace by the cyclic and filtering paths of our method. In addition, thehuman-in-the-loop based three-phase approach provides a framework for theimprovement of the motion retargeting policy by both quantitative andqualitative manners. Using the proposed C-3PO method, we were successfully ableto learn the motion retargeting skill between the human skeleton and motion ofthe multiple robots such as NAO, Pepper, Baxter and C-3PO.

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning