Uncertainty in Action: Confidence Elicitation in Embodied Agents

2025-03-13

AI Summary

Embodied agents struggle to accurately quantify decision confidence in dynamic, multimodal, open-ended environments (e.g., Minecraft), primarily due to unmodeled perceptual and inferential uncertainties—especially across inductive, deductive, and abductive reasoning pathways. Method: We propose the first embodied confidence elicitation framework, introducing Elicitation Policies (tailored to the three reasoning structures) and Execution Policies (enabling scene reinterpretation, multi-strategy action sampling, and hypothesis/counterfactual reasoning). The framework integrates chain-of-thought prompting, confidence calibration mechanisms, and end-to-end training. Contribution/Results: Experiments demonstrate significant improvements in confidence calibration and failure prediction accuracy. We further identify abductive uncertainty modeling as a critical bottleneck. This work establishes a reproducible benchmark, a formal theoretical framework, and a principled technical pathway toward trustworthy embodied AI.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Challenges in expressing confidence for embodied agents in dynamic environments.
• Need for structured confidence assessment across different reasoning types.
• Persistent difficulties in distinguishing uncertainty, especially in abductive reasoning.

Innovation

• Elicitation Policies structure confidence assessment.
• Execution Policies enhance confidence calibration.
• Chain-of-Thoughts improves confidence calibration.