Multi-Agent Reinforcement Learning (MARL) provides a powerful framework for learning coordination in multi-agent systems. However, applying MARL to robotics remains challenging due to their high-dimensional continuous joint action spaces, complex reward design, and non-stationarity from concurrently learning agents. On the other hand, humans often learn complex coordination with the help of coaches, who guide learning through carefully designed curricula and detailed feedback. Building on the reasoning capabilities of foundation models, we argue that these models can similarly coach robots to learn coordination. Motivated by this, we propose CRAFT: Coaching Reinforcement learning Autonomously using Foundation models for learning coordination Tasks, a framework that leverages foundation models to act as a "coach" for multi-robot coordination. CRAFT automatically decomposes long-horizon coordination tasks into sequences of subtasks using the planning capability of Large Language Models (LLMs). Then, CRAFT trains each subtask using LLM-generated reward functions, and refines them through a Vision Language Model (VLM)-guided reward-refinement loop. We evaluate CRAFT on multi-quadruped navigation and bimanual manipulation tasks, and demonstrate its capability to learn complex coordination behaviors. In addition, in a multi-quadruped navigation setting, we show that our learned policies transfer to the real world. Project website is https://iconlab.negarmehr.com/CRAFT/

翻译：暂无翻译