COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
<|reference_start|>COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models: Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results. However, they mainly focus on single or multiple homogeneous robots on simple tasks. Practically, complex long-horizon tasks always require collaborations among multiple heterogeneous robots especially with more complex action spaces, which makes these tasks more challenging. To this end, we propose COHERENT, a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems including quadrotors, robotic dogs, and robotic arms. Specifically, a Proposal-Execution-Feedback-Adjustment (PEFA) mechanism is designed to decompose and assign actions for individual robots, where a centralized task assigner makes a task planning proposal to decompose the complex task into subtasks, and then assigns subtasks to robot executors. Each robot executor selects a feasible action to implement the assigned subtask and reports self-reflection feedback to the task assigner for plan adjustment. The PEFA loops until the task is completed. Moreover, we create a challenging heterogeneous multi-robot task planning benchmark encompassing 100 complex long-horizon tasks. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency. The experimental videos, code, and benchmark are released at https://github.com/MrKeee/COHERENT.<|reference_end|>
@article{liu2024coherent:,
    title={COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large
  Language Models},
    author={Kehui Liu, Zixin Tang, Dong Wang, Zhigang Wang, Bin Zhao, Xuelong Li},
    journal={arXiv preprint arXiv:2409.15146},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15146},
    primaryClass={cs.RO cs.AI}
}