用事后评估提升多机器人协作决策效率
Integrating Retrospective Framework in Multi-Robot Collaboration
- 基于观察与指令实时决策，事后评估反馈优化
- 仿真测试中任务表现和适应性显著提升
- 适合动态不确定环境下的机器人协同研究
大型语言模型（LLMs）在提升多机器人系统通信与协调方面展现出巨大潜力。然而，现有方法在动态不确定环境中仍难以实现高效协作与决策，这在真实多机器人场景中尤为常见。为此，我们提出一种新型的回顾式演员-评论家框架，用于多机器人协作。该框架包含两个核心组件：(1) 基于观测和任务指令进行实时决策的演员；(2) 事后评估结果以提供反馈，实现持续优化的评论家。通过在模拟环境中的大量实验验证，该方法显著提升了任务性能与适应能力。本工作为机器人协作中的长期挑战提供了稳健解决方案。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi-robot systems. However, existing methods often struggle to achieve efficient collaboration and decision-making in dynamic and uncertain environments, which are common in real-world multi-robot scenarios. To address these challenges, we propose a novel retrospective actor-critic framework for multi-robot collaboration. This framework integrates two key components: (1) an actor that performs real-time decision-making based on observations and task directives, and (2) a critic that retrospectively evaluates the outcomes to provide feedback for continuous refinement, such that the proposed framework can adapt effectively to dynamic conditions. Extensive experiments conducted in simulated environments validate the effectiveness of our approach, demonstrating significant improvements in task performance and adaptability. This work offers a robust solution to persistent challenges in robotic collaboration.
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