Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework

2024-11-03

arXiv.org

AI Summary

Multimodal foundation models in robot perception-planning suffer from insufficient task reliability due to inherent uncertainties in perceptual interpretation and decision generation. Method: This paper proposes the first unified framework integrating decoupled modeling, quantitative uncertainty assessment, and active intervention. It introduces a novel decoupled representation of perception and decision uncertainties; employs conformal prediction for perception calibration and formal-methods-driven decision uncertainty quantification (FMDP); and designs dual intervention mechanisms—active re-observation and self-refinement of high-confidence data. Contribution/Results: Evaluated on both real-world and simulated robotic tasks, the framework improves task success rate by 5% and reduces planning outcome variance by 40%, significantly enhancing system robustness and reliability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multimodal foundation models offer a promising framework for robotic perception and planning by processing sensory inputs to generate actionable plans. However, addressing uncertainty in both perception (sensory interpretation) and decision-making (plan generation) remains a critical challenge for ensuring task reliability. We present a comprehensive framework to disentangle, quantify, and mitigate these two forms of uncertainty. We first introduce a framework for uncertainty disentanglement, isolating perception uncertainty arising from limitations in visual understanding and decision uncertainty relating to the robustness of generated plans. To quantify each type of uncertainty, we propose methods tailored to the unique properties of perception and decision-making: we use conformal prediction to calibrate perception uncertainty and introduce Formal-Methods-Driven Prediction (FMDP) to quantify decision uncertainty, leveraging formal verification techniques for theoretical guarantees. Building on this quantification, we implement two targeted intervention mechanisms: an active sensing process that dynamically re-observes high-uncertainty scenes to enhance visual input quality and an automated refinement procedure that fine-tunes the model on high-certainty data, improving its capability to meet task specifications. Empirical validation in real-world and simulated robotic tasks demonstrates that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines. These improvements are attributed to the combined effect of both interventions and highlight the importance of uncertainty disentanglement, which facilitates targeted interventions that enhance the robustness and reliability of autonomous systems. Fine-tuned models, code, and datasets are available at https://uncertainty-in-planning.github.io/.

Problem

• Disentangle uncertainty in multimodal robotic perception and planning
• Quantify perception and decision uncertainty using tailored methods
• Mitigate uncertainty via active sensing and automated model refinement

Innovation

• Uncertainty disentanglement framework for perception and decision-making
• Conformal prediction for calibrating perception uncertainty
• Formal-Methods-Driven Prediction for quantifying decision uncertainty