利用大语言模型实现异构自组织团队协作
Leveraging Large Language Model for Heterogeneous Ad Hoc Teamwork Collaboration
AI总结：
针对异构自组织团队中自组织机器人无预协调与未知队友协作的难题，提出基于LLM与IRoT的无训练分层规划去中心化框架，经基准实验与真实机器人验证可有效提升团队协作效率。
摘要：
与被广泛研究的同构多机器人协作相比，具备不同能力的异构机器人能够为更复杂的任务提供更高效、灵活的协作模式。本文研究了一个更具挑战性的异构自组织团队协作问题：一台自组织机器人加入一个已有的异构团队，为共同目标开展协作。具体而言，该自组织机器人需在无预先协调的情况下与未知队友协作，且需生成合理的协作策略以提升整个团队的效率。为解决这一难题，我们利用大语言模型（LLM）的显著潜力，构建了一个去中心化的异构自组织团队协作框架，核心是为自组织机器人生成与原有异构队友协作的合理策略。我们结合LLM与新提出的思维交互式反思（IRoT）方法，为自组织智能体开发了一种无需训练的分层动态规划器，使其能够适配不同的团队。我们还构建了一个基准测试数据集，用于在异构自组织多智能体整理任务中评估所提框架。我们在该基准上开展了大量对比实验与消融实验，验证了所提框架的有效性。我们还将该框架应用于真实场景中的实体机器人，实验视频可在https://youtu.be/wHYP5T2WIp0查看。
英文摘要：
Compared with the widely investigated homogeneous multi-robot collaboration, heterogeneous robots with different capabilities can provide a more efficient and flexible collaboration for more complex tasks. In this paper, we consider a more challenging heterogeneous ad hoc teamwork collaboration problem where an ad hoc robot joins an existing heterogeneous team for a shared goal. Specifically, the ad hoc robot collaborates with unknown teammates without prior coordination, and it is expected to generate an appropriate cooperation policy to improve the efficiency of the whole team. To solve this challenging problem, we leverage the remarkable potential of the large language model (LLM) to establish a decentralized heterogeneous ad hoc teamwork collaboration framework that focuses on generating reasonable policy for an ad hoc robot to collaborate with original heterogeneous teammates. A training-free hierarchical dynamic planner is developed using the LLM together with the newly proposed Interactive Reflection of Thoughts (IRoT) method for the ad hoc agent to adapt to different teams. We also build a benchmark testing dataset to evaluate the proposed framework in the heterogeneous ad hoc multi-agent tidying-up task. Extensive comparison and ablation experiments are conducted in the benchmark to demonstrate the effectiveness of the proposed framework. We have also employed the proposed framework in physical robots in a real-world scenario. The experimental videos can be found at https://youtu.be/wHYP5T2WIp0.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
