让小模型学会避障和可达性，提升多机器人控制可靠性
Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners
- 用强化学习奖励有效动作，逼小模型理解物理约束
- 在BoxNet和BoxNet3D上，小模型性能超越大模型
- 适合需要安全可靠的低成本多机器人系统应用
大语言模型在机器人控制任务中表现优异，但实际部署受限。即使是GPT-o4mini等先进模型，也常生成违反物理约束的无效动作，如指向不可达位置或引发机器人碰撞。问题根源在于推理过程中缺乏对物理约束的认知。为此，我们提出一种融合可验证奖励的强化学习框架（RLVR），通过仅对成功完成任务的有效动作给予正向奖励，引导小模型习得约束感知能力。实验使用两个小型模型：非推理型Qwen2.5-3B-Instruct和推理型Qwen3-4B。结果表明，在BoxNet任务和基于MuJoCo构建的BoxNet3D新环境中，具备约束感知的小模型显著优于未加约束的大模型。该工作证明，通过物理约束接地，即使小模型也能实现高效、可扩展的复杂环境多机器人控制。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even state-ofthe-art LLMs, such as GPT-o4mini, frequently produce invalid action plans that violate physical constraints, such as directing a robot to an unreachable location or causing collisions between robots. This issue primarily arises from a lack of awareness of these physical constraints during the reasoning process. To address this issue, we propose a novel framework that integrates reinforcement learning with verifiable rewards (RLVR) to incentivize knowledge of physical constraints into LLMs to induce constraints-aware reasoning during plan generation. In this approach, only valid action plans that successfully complete a control task receive positive rewards. We applied our method to two small-scale LLMs: a non-reasoning Qwen2.5-3B-Instruct and a reasoning Qwen3-4B. The experiment results demonstrate that constraint-aware small LLMs largely outperform large-scale models without constraints, grounded on both the BoxNet task and a newly developed BoxNet3D environment built using MuJoCo. This work highlights the effectiveness of grounding even small LLMs with physical constraints to enable scalable and efficient multi-robot control in complex, physically constrained environments.
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