Late Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse
Late Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse
This research paper introduces 'Late Autumn,' a method where Large Language Model (LLM) coding agents autonomously specialize a general multi-robot planner for a specific warehouse environment. By trading generality for throughput, the agents achieve significant performance improvements (22-60% increase in throughput) on unseen seeds and rediscover known Multi-Agent Path Finding (MAPF) mechanisms like corridor-exit priorities. The study evaluates six campaigns on a lifelong planner modeled after an existing automated warehouse, finding that while gains carry over to different layouts and fleet sizes, they diminish with smaller fleets or outbound-heavy demand. The generated code patches are reviewable, allowing experts to verify changes, though some modifications can increase compute requirements by over 100x.
The study demonstrates that LLM-based coding agents can specialize a general multi-robot planner for a specific warehouse layout, achieving throughput gains of 22–60% on unseen seeds while rediscovering known MAPF mechanisms; however, performance degrades under outbound-heavy demand mixes and some generated patches significantly increase compute requirements.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
compute multiplier
>100
View original evidence
nds problems that the score misses; some patches multiply planner compute by over 100x. LLM agents can therefore turn each site's bias into extra throughput at little engineering cost, with reviewable patches; we expect experts to shift toward designing insightful metrics and fast simulators that mirror the real problem.
Open source S5median step size
27
View original evidence
-exit priorities, local guidance, and rollouts, and independent campaigns find the same bottleneck; (3) the gains largely carry over to another layout, a larger fleet, and other service times, shrink with a smaller fleet, and mostly vanish under an outbound-heavy demand mix; and (4) each final patch (0.7k--2.4k lines) is a chain of scored commits with a median step of 27 lines, so experts can review it, and review fi
Open source S4
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
or one site, trading generality for throughput, much as post-training adapts a pretrained LLM to one task. We study six campaigns on a lifelong planner that combines task assignment with multi-agent path finding, on a floor modeled on an existing automated warehouse. We find that (1) the agents raise throughput by 22--60%, and the gains hold on unseen seeds; (2) they rediscover known MAPF mechanisms, such as corridor
Open source S3
-exit priorities, local guidance, and rollouts, and independent campaigns find the same bottleneck; (3) the gains largely carry over to another layout, a larger fleet, and other service times, shrink with a smaller fleet, and mostly vanish under an outbound-heavy demand mix; and (4) each final patch (0.7k--2.4k lines) is a chain of scored commits with a median step of 27 lines, so experts can review it, and review fi
Open source S4
nds problems that the score misses; some patches multiply planner compute by over 100x. LLM agents can therefore turn each site's bias into extra throughput at little engineering cost, with reviewable patches; we expect experts to shift toward designing insightful metrics and fast simulators that mirror the real problem.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org