Embodied Agent Confidence Elicitation in Dynamic Multimodal Environments

This research introduces a novel framework using elicitation and execution policies to improve how embodied AI agents express their confidence in complex, open-ended environments like Minecraft. The authors demonstrate that structured reasoning techniques, such as Chain-of-Thought and Plan & Solve, significantly enhance an agent's ability to calibrate its confidence and predict failures. While challenges remain, particularly in abductive reasoning and model consistency, the proposed policies offer a promising approach for more reliable uncertainty assessment in embodied agents.