Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers

2026-02-27

AI Summary

This work addresses the challenge of carton consolidation in human–robot collaborative containerized fulfillment centers, where processing speed, resource consumption, and space utilization must be jointly optimized under complex operational constraints. The problem is formulated as a multi-objective constrained reinforcement learning task in a high-dimensional state space. To tackle it, the authors propose a single-policy learning framework that integrates regret-free dynamics with minimax optimization. By leveraging Lagrangian duality and an optimal response mechanism, the approach simultaneously satisfies all constraints—even in the absence of theoretical guarantees—and effectively mitigates error cancellation and policy oscillation inherent in time-averaged solutions. Extensive simulations in real-world warehouse environments demonstrate that the learned single policy efficiently balances competing objectives, highlighting the practical potential of multi-objective reinforcement learning in large-scale industrial decision-making systems.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Optimizing the consolidation process in container-based fulfillment centers requires trading off competing objectives such as processing speed, resource usage, and space utilization while adhering to a range of real-world operational constraints. This process involves moving items between containers via a combination of human and robotic workstations to free up space for inbound inventory and increase container utilization. We formulate this problem as a large-scale Multi-Objective Reinforcement Learning (MORL) task with high-dimensional state spaces and dynamic system behavior. Our method builds on recent theoretical advances in solving constrained RL problems via best-response and no-regret dynamics in zero-sum games, enabling principled minimax policy learning. Policy evaluation on realistic warehouse simulations shows that our approach effectively trades off objectives, and we empirically observe that it learns a single policy that simultaneously satisfies all constraints, even if this is not theoretically guaranteed. We further introduce a theoretical framework to handle the problem of error cancellation, where time-averaged solutions display oscillatory behavior. This method returns a single iterate whose Lagrangian value is close to the minimax value of the game. These results demonstrate the promise of MORL in solving complex, high-impact decision-making problems in large-scale industrial systems.

Problem

• Multi-Objective Reinforcement Learning
• Tote Allocation
• Human-Robot Collaboration
• Fulfillment Centers
• Constrained Optimization

Innovation

• Multi-Objective Reinforcement Learning
• Constrained RL
• Best-Response Dynamics
• No-Regret Learning
• Minimax Policy