AI system learns to keep warehouse multiple robots traffic running smoothly

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"Inside autonomous warehouses, even small traffic jams or minor collisions can snowball into massive slowdowns. Now, MIT researchers have developed a system that keeps a fleet of robots moving smoothly. It decides which bots should get the right of way at every moment, avoiding congestion and increasing throughput."

From the abstract:

"Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the overall system throughput. However, the complexity of warehouse environments and the long-term dynamics of lifelong MAPF often demand costly adaptations to classical search-based solvers. While machine learning methods have been explored, their superiority over search-based methods remains inconclusive.

In this paper, we introduce Reinforcement Learning (RL) guided Rolling Horizon Prioritized Planning (RL-RH-PP), the first framework integrating RL with search-based planning for lifelong MAPF.

Specifically, we leverage classical Prioritized Planning (PP) as a backbone for its simplicity and flexibility in integrating with a learning-based priority assignment policy.

By formulating dynamic priority assignment as a Partially Observable Markov Decision Process (POMDP), RL-RH-PP exploits the sequential decision-making nature of lifelong planning while delegating complex spatial-temporal interactions among agents to reinforcement learning.

An attention-based neural network autoregressively decodes priority orders on-the-fly, enabling efficient sequential single-agent planning by the PP planner. Evaluations in realistic warehouse simulations show that RL-RH-PP achieves the highest total throughput among baselines and generalizes effectively across agent densities, planning horizons, and warehouse layouts.

Our interpretive analyses reveal that RL-RH-PP proactively prioritizes congested agents and strategically redirects agents from congestion, easing traffic flow and boosting throughput. These findings highlight the potential of learning-guided approaches to augment traditional heuristics in modern warehouse automation."

AI system learns to keep warehouse robot traffic running smoothly | MIT News | Massachusetts Institute of Technology "This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput."

Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation (no public access)