On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer

arXiv:2312.03673v2 Announce Type: replace-cross
Abstract: We study the choice of action space in robot manipulation learning and sim-to-real transfer. We define metrics that assess the performance, and examine the emerging properties in the different action spaces. We train over 250 reinforcement learning~(RL) agents in simulated reaching and pushing tasks, using 13 different control spaces. The choice of spaces spans combinations of common action space design characteristics. We evaluate the training performance in simulation and the transfer to a real-world environment. …

abstract agents arxiv cs.lg cs.ro manipulation metrics performance reinforcement reinforcement learning robot robot manipulation role sim space spaces study train transfer type