Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models. (arXiv:2111.13129v2 [cs.RO] UPDATED)

A core challenge for an autonomous agent acting in the real world is to adapt
its repertoire of skills to cope with its noisy perception and dynamics. To
scale learning of skills to long-horizon tasks, robots should be able to learn
and later refine their skills in a structured manner through trajectories
rather than making instantaneous decisions individually at each time step. To
this end, we propose the Soft Actor-Critic Gaussian Mixture Model (SAC-GMM), a
novel hybrid approach that learns …

actor-critic arxiv robot