Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation

arXiv:2203.09749v2 Announce Type: replace-cross
Abstract: Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledge about stable and dexterous manipulation skills. This paper presents a goal-conditioned dual-action (GC-DA) deep imitation learning (DIL) approach that can learn dexterous manipulation skills using human demonstration data. Previous DIL methods map the current sensory input and reactive action, which often fails because of compounding errors in imitation …

abstract arm arxiv cs.cv cs.ro horizon imitation learning knowledge manipulation modeling object objects paper robot robot manipulation skills type