E2Map: 言語モデルを用いた自己内省的ロボットナビゲーションのための経験・感情マップ
E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
ロボットが実環境での経験と感情的反応をマップに統合し、言語モデルによる初期計画を一度の経験で修正することで、確率的環境でのナビゲーション性能を向上させる手法を提案。
著者: Chan Kim, Keonwoo Kim, Mintaek Oh, Hanbi Baek, Jiyang Lee, Donghwi Jung, Soojin Woo, Younkyung Woo, John Tucker, Roya Firoozi, Seung-Woo Seo, Mac Schwager, Seong-Woo Kim
分類: cs.RO, cs.AI
原文アブストラクト
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/.
