用多个AI代理协作提升机器人任务执行的准确性和安全性。
Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction
- 设计多个大语言模型代理协同工作，共同规划与验证任务指令。
- 协作架构显著减少代码错误，提升抽象问题解决能力。
- 适合关注人机交互安全与可靠性的研究者与开发者。
随着大型语言模型（LLMs）的发展，人类与机器人助手的自然语言交互方式有了新的可能。这些模型需能将自然语言指令转化为有效、恰当且安全的机器人操作。然而，实际中模型常出现幻觉，导致任务偏差或安全隐患。在其他领域，通过多个LLM代理协作进行联合规划、编程和自我检查，已有效缓解此类问题。本研究对比了单个独立代理与多个协作代理在人机交互中的表现，结果表明代理数量与成功率之间无明确趋势。但某些协作架构显著提升了生成无错代码的能力，并更擅长解决抽象问题。
原文摘要 · Abstract (English)
With the recent development of natural language generation models - termed as large language models (LLMs) - a potential use case has opened up to improve the way that humans interact with robot assistants. These LLMs should be able to leverage their large breadth of understanding to interpret natural language commands into effective, task appropriate and safe robot task executions. However, in reality, these models suffer from hallucinations, which may cause safety issues or deviations from the task. In other domains, these issues have been improved through the use of collaborative AI systems where multiple LLM agents can work together to collectively plan, code and self-check outputs. In this research, multiple collaborative AI systems were tested against a single independent AI agent to determine whether the success in other domains would translate into improved human-robot interaction performance. The results show that there is no defined trend between the number of agents and the success of the model. However, it is clear that some collaborative AI agent architectures can exhibit a greatly improved capacity to produce error-free code and to solve abstract problems.
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