Can LLMs learn to control multiple robots to push large objects?
Learning Multi-Agent Collaborative Manipulation for Long-Horizon Quadrupedal Pushing
November 12, 2024
https://arxiv.org/pdf/2411.07104This paper presents a hierarchical multi-agent reinforcement learning (MARL) system for coordinating multiple quadrupedal robots to push large objects to target locations in environments with obstacles. The system uses a high-level controller for global planning and subgoal generation, a mid-level controller for decentralized robot coordination towards subgoals, and a pre-trained low-level controller for locomotion.
Key points for LLM-based multi-agent systems:
- Hierarchical control: Demonstrates the effectiveness of a hierarchical structure for complex multi-agent tasks, which can be relevant to LLMs managing multiple agents with different roles and responsibilities.
- Decentralized execution: The mid-level controller showcases decentralized execution based on shared subgoals, allowing for scalable multi-agent coordination, which is crucial for large LLM-based systems.
- Adaptive planning: The incorporation of an adaptive policy in the high-level controller allows for dynamic adjustments to plans based on environmental changes and object states, similar to how LLMs can adapt their strategies based on evolving contexts.
- Goal-conditioned policies: The mid-level controller uses goal-conditioned policies to guide robots towards subgoals, a concept analogous to prompting LLMs to achieve specific objectives.
- Sim-to-real transfer: The research shows successful deployment on real robots, highlighting the potential of simulated training for complex LLM-based multi-agent applications.