TRANS-AM: Transfer Learning by Aggregating Dynamics Models for Soft Robotic Assembly
Kazutoshi Tanaka Ryo Yonetani Masashi Hamaya Robert Lee Felix von Drigalski Yoshihisa Ijiri Kazutoshi Tanaka Ryo Yonetani Masashi Hamaya Robert Lee Felix von Drigalski Yoshihisa Ijiri
Practical industrial assembly scenarios often require robotic agents to adapt their skills to unseen tasks quickly. While transfer reinforcement learning (RL) could enable such quick adaptation, much prior work has to collect many samples from source environments to learn target tasks in a model-free fashion, which still lacks sample efficiency on a practical level. In this work, we develop a nove...