Expressing confidence is challenging for embodied agents navigating dynamic multimodal environments, where uncertainty arises from both perception and decision-making processes. We present the first work investigating embodied confidence elicitation in open-ended multimodal environments. We introduce Elicitation Policies, which structure confidence assessment across inductive, deductive, and abductive reasoning, along with Execution Policies, which enhance confidence calibration through scenario reinterpretation, action sampling, and hypothetical reasoning. Evaluating agents in calibration and failure prediction tasks within the Minecraft environment, we show that structured reasoning approaches, such as Chain-of-Thoughts, improve confidence calibration. However, our findings also reveal persistent challenges in distinguishing uncertainty, particularly under abductive settings, underscoring the need for more sophisticated embodied confidence elicitation methods.

本研究解决了具身智能体在动态多模态环境中表达信心的难题。提出的信心引导策略和执行策略通过结构化推理与场景重解释，提高了信心校准能力。研究发现，尽管采用结构化推理方法能够改善信心校准，但在归纳推理下仍存在区分不确定性的挑战，突显了对更复杂的引导方法的需求。

行动中的不确定性：具身智能体的信心引导