让机器人通过经验与情绪反馈实时优化导航，提升复杂环境适应力。
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
- 构建经验-情绪地图，融合语言模型知识与真实交互体验
- 在随机环境中任务成功率提升显著，支持一次调整即适配新情况
- 适合需要自主迭代的机器人导航场景，尤其动态环境应用
大型语言模型（LLMs）在指导具身智能体执行语言指令方面展现出巨大潜力，涵盖机器人操作与导航等任务。然而，现有方法主要针对静态环境设计，未利用智能体自身经验来优化初始规划。由于现实环境具有固有的随机性，仅依赖LLM通用知识的初始计划在动态场景中易失败。为此，本文提出经验-情绪地图（E2Map），融合语言模型知识与智能体的真实世界体验，借鉴人类情感反馈机制。该方法可通过一次经验更新实现行为自适应调整。在包括仿真与真实场景在内的随机导航环境中评估表明，相比现有基于LLM的方法，该方法在随机环境中的表现显著提升。代码与补充材料见 https://e2map.github.io/。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. Code and supplementary materials are available at https://e2map.github.io/.
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