Title: Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners
Primary area: applications to robotics, autonomy, planning
Keywords: LLM, Robotic Control, Large Language Model, Task Planning

Abstract: Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even state-of-the-art LLMs, such as GPT-5, 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 constraint knowledge training, grounded on both the BoxNet task and a newly developed BoxNet3D environment built using MuJoCo, which involves LLM planning for up to 25 robots. 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.