Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
<|reference_start|>Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments: The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate compared to other state-of-the-art LM-based multi-agent planners.<|reference_end|>
@article{nayak2024long-horizon,
    title={Long-Horizon Planning for Multi-Agent Robots in Partially Observable
  Environments},
    author={Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Vittal
  Thirumalai, Jackson Zhang, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik
  Gopalakrishnan, James Harrison, Brian Ichter, Anuj Mahajan, Hamsa
  Balakrishnan},
    journal={arXiv preprint arXiv:2407.10031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10031},
    primaryClass={cs.RO cs.MA}
}