Learning Manipulation Tasks in Dynamic and Shared 3D Spaces

arXiv:2404.17673v1 Announce Type: new
Abstract: Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. manipulators) in the workplace and among human operators. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal …

abstract arxiv automated autonomous autonomous systems collaborative cs.lg cs.ro dynamic every manipulation materials operations process sector segregation spaces systems tasks type workplace