Seeing with Partial Certainty: Conformal Prediction for Robotic Scene Recognition in Built Environments

2025-01-09

AI Summary

Vision-language models (VLMs) deployed in assistive robotics for indoor scene recognition suffer from hallucination and poor robustness to ambiguous human instructions, leading to high identification uncertainty and unreliable confidence estimates. Method: This paper proposes the first conformal prediction-based uncertainty alignment framework specifically designed for VLM-driven scene recognition—requiring no architectural modification or fine-tuning, and enabling zero-shot, plug-and-play deployment on Matterport3D. Contribution/Results: The framework simultaneously provides statistically rigorous uncertainty quantification, interpretable uncertainty assessment, and principled active query decisions (e.g., requesting human clarification). Experiments demonstrate significant improvements in task success rate, substantial reduction in manual intervention frequency, and strong robustness and practicality in complex, real-world indoor environments—all while guaranteeing valid coverage under finite-sample statistical significance.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In assistive robotics serving people with disabilities (PWD), accurate place recognition in built environments is crucial to ensure that robots navigate and interact safely within diverse indoor spaces. Language interfaces, particularly those powered by Large Language Models (LLM) and Vision Language Models (VLM), hold significant promise in this context, as they can interpret visual scenes and correlate them with semantic information. However, such interfaces are also known for their hallucinated predictions. In addition, language instructions provided by humans can also be ambiguous and lack precise details about specific locations, objects, or actions, exacerbating the hallucination issue. In this work, we introduce Seeing with Partial Certainty (SwPC) - a framework designed to measure and align uncertainty in VLM-based place recognition, enabling the model to recognize when it lacks confidence and seek assistance when necessary. This framework is built on the theory of conformal prediction to provide statistical guarantees on place recognition while minimizing requests for human help in complex indoor environment settings. Through experiments on the widely used richly-annotated scene dataset Matterport3D, we show that SwPC significantly increases the success rate and decreases the amount of human intervention required relative to the prior art. SwPC can be utilized with any VLMs directly without requiring model fine-tuning, offering a promising, lightweight approach to uncertainty modeling that complements and scales alongside the expanding capabilities of foundational models.

Problem

• Robotics
• Assistive Technology
• Environmental Perception

Innovation

• Partial Deterministic Horizon
• Uncertainty Handling
• Seamless Integration