Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking

arXiv:2404.08006v1 Announce Type: cross
Abstract: In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimization problem in such systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel multi-objective Deep Reinforcement Learning (DRL) approach to learn effective allocation policies to maximize pick efficiency while also aiming to improve …

abstract arxiv autonomous autonomous mobile robots collaborative cs.ai cs.lg cs.ro fair human locations math.oc mobile optimization paper policies robot robots systems through travel type uncertainty warehouse