Uncertainty-Resilient Active Intention Recognition for Robotic Assistants

2025-08-26

AI Summary

Robotic assistants in real-world settings face uncertain intent recognition due to perceptual noise and incomplete information; existing approaches either rely on explicit instructions—compromising autonomy—or assume perfect perception, limiting generalizability. This paper proposes an uncertainty-resilient framework grounded in intention-aware POMDPs, the first to unify active perception and intent inference within a partially observable decision-theoretic model. By fusing real-time multimodal sensor data, integrating multiple planners, and combining hierarchical planning with Bayesian inference, the framework enables robust intent estimation and collaborative decision-making under dynamic, noisy conditions. Experiments on a physical robot platform demonstrate significant improvements in intent recognition accuracy and human–robot interaction fluency, validating the framework’s strong adaptability to perceptual uncertainty and high degree of operational autonomy.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts, which limits autonomy, or is oversimplified through assumptions such as near-perfect information. We argue that a critical gap remains unaddressed -- specifically, the challenge of reasoning about the uncertain outcomes and perception errors inherent to human intention recognition. In response, we present a framework designed to be resilient to uncertainty and sensor noise, integrating real-time sensor data with a combination of planners. Centered around an intention-recognition POMDP, our approach addresses cooperative planning and acting under uncertainty. Our integrated framework has been successfully tested on a physical robot with promising results.

Problem

• Active intention recognition resilient to uncertainty
• Addressing perception errors in human intention recognition
• Cooperative planning under uncertain outcomes

Innovation

• Uncertainty-resilient framework with real-time sensors
• POMDP for intention recognition under uncertainty
• Integrated cooperative planning with sensor noise resilience