MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation

arXiv:2311.08393v3 Announce Type: replace-cross
Abstract: The learn-from-observation (LfO) paradigm is a human-inspired mode for a robot to learn to perform a task simply by watching it being performed. LfO can facilitate robot integration on factory floors by minimizing disruption and reducing tedious programming. A key component of the LfO pipeline is a transformation of the depth camera frames to the corresponding task state and action pairs, which are then relayed to learning techniques such as imitation or inverse reinforcement learning …

abstract action recognition arxiv cs.ai cs.cv cs.lg cs.ro disruption factory human integration key learn observation paradigm pipeline programming recognition robot robust state trajectory type view