Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint

arXiv:2404.05051v1 Announce Type: new
Abstract: We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decision processes. The spectral decomposition brings about representation that can linearly represent the state-action value function induced by any policies, thus can be regarded as skills. The skill representations are transferable across arbitrary tasks with the same transition dynamics. Moreover, to handle the sim-to-real gap in the dynamics, we propose a …

abstract arxiv context control cs.lg cs.ro decision discovery function inspiration markov policies processes representation representation learning robotics sim state study transfer type value