Problem
Research questions and friction points this paper is trying to address.
Multi-agent Reinforcement Learning
Warehouse Robot Coordination
Communication Loss
Safety Constraints
Hierarchical Multi-agent Reinforcement Learning for Warehouse Robot Coordination under Communication Loss
🤖 AI Summary
This study addresses the challenge of swarm coordination in warehouse multi-robot systems under communication loss. It proposes a hierarchical multi-agent reinforcement learning framework that integrates grouped centralized and distributed cooperative mechanisms. Recurrent neural networks are employed to predict missing information, thereby maintaining state awareness, while a high-level policy guides local control augmented by predictive safety filters to enforce behavioral constraints. This architecture innovatively unifies hierarchical decision-making, information compensation, and safety control. Experimental results demonstrate that the proposed approach significantly improves task completion rates in communication-limited scenarios, effectively curbs the growth of communication overhead, and ensures operational safety throughout system execution.
📝 Abstract
In this paper, we propose a hierarchical multi-agent reinforcement learning framework for coordinating robot teams in warehouse environments under communication loss. We partition the robot team into groups, with centralized coordination within each group and distributed coordination across groups. Each group uses a recurrent predictor to estimate unavailable interaction information due to communication loss. A higher-level policy then generates a compact coordination reference that conditions the local control policy within each group. A predictive safety filter evaluates and modifies the proposed controls when they violate safety constraints. Simulation results show improved task completion under communication loss, reduced communication growth as the team size increases, and safe operation in the tested scenarios.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical Multi-agent Reinforcement Learning
Communication Loss
Recurrent Predictor
Predictive Safety Filter
Warehouse Robot Coordination
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