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

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/.

• The paper introduces a formal framework that disentangles perception and decision uncertainty in multimodal foundation models for planning.
• It employs conformal prediction for calibrating visual confidence and FMDP for verifying plan adherence, reducing error propagation.
• Empirical results show up to 40% variability reduction and a 5% increase in task success, demonstrating improved system robustness.

Understanding Uncertainty in Planning with Multimodal Foundation Models: A Formal Framework

The paper by Bhatt et al. provides a formal framework aimed at enhancing the robustness and reliability of multimodal foundation models used in robotic perception and planning, specifically by addressing key uncertainties. The motivation stems from the inherent uncertainties present in perception from sensory inputs such as images and the decision-making processes that follow in generating plans. These uncertainties can significantly impact the performance and dependability of autonomous systems.

Uncertainty Disentanglement Framework

The authors introduce a critical distinction between two types of uncertainty: perception uncertainty and decision uncertainty. Perception uncertainty arises due to limitations in interpreting visual data, whereas decision uncertainty relates to the robustness of the generated plan. This clear separation allows for targeted interventions that precisely address the specific challenges posed by each uncertainty type.

• Perception Uncertainty: The framework employs conformal prediction to calibrate perception uncertainty, providing formal statistical guarantees about the correctness of sensory interpretations. Through this calibration, the model receives a quantifiable measure of its visual confidence, enabling it to better assess and articulate the accuracy of perceived information under varying conditions.
• Decision Uncertainty: To quantify decision uncertainty, the authors introduce Formal-Methods-Driven Prediction (FMDP), which uses tools from formal verification to establish theoretical guarantees about the adherence of generated plans to task specifications. This approach offers a robust framework for validating the outputs of planning models and ensuring that they align with predefined operational criteria.

Targeted Interventions for Enhanced Robustness

Based on the quantification of uncertainties, Bhatt et al. propose two main interventions to enhance the performance of multimodal foundation models:

• Active Sensing: This mechanism dynamically adjusts the sensory input process through repeated observations of scenes identified as high-uncertainty, thereby improving the quality of visual data processed by the model. By re-observing such scenes, the framework achieves a reduction in the propagation of perceptual errors into the decision-making phase.
• Automated Refinement: The framework incorporates an uncertainty-aware refinement procedure that strengthens the model by selectively fine-tuning it on low-uncertainty samples. This process refines the model’s alignment with task specifications while reducing reliance on exhaustive human annotations, thereby improving scalability.

Empirical Validation and Practical Implications

The framework was validated through empirical experiments involving both real-world and simulated robotic tasks. The results indicated a reduction in variability by up to 40% and an enhancement in task success rates by 5% compared to baseline approaches. These results are attributed to the effective combination of active sensing and automated refinement, underscoring the significance of clearly distinguishing between perception and decision uncertainties.

The implications of this research are noteworthy in both practical and theoretical contexts. Practically, the framework provides a structured approach to improving the reliability of autonomous systems in dynamic environments by addressing uncertainty as a critical factor. Theoretically, it advances our understanding of uncertainty management in multimodal foundation models, paving the way for future research to explore additional sources of uncertainty and more sophisticated intervention techniques.

Speculation on Future Developments in AI

Progress in this arena could lead to more seamless integration of foundation models within broader AI systems, potentially improving domains such as autonomous vehicles, robotics in dynamic environments, and human-assistive technologies. Further research might explore integrating the framework with other forms of uncertainty quantification methods or expanding the applications to other modalities beyond visual inputs, such as audio or haptic data, thereby enriching multimodal machine learning systems.

By providing a formal and systematic approach to addressing the challenges of uncertainty in multimodal foundation models, the framework contributes to ongoing efforts to make AI systems more dependable and effective. The research offers an encouraging step toward tackling complex planning and decision-making tasks in uncertain environments, suggesting various avenues for future exploration and innovation.