Coupled Particle Filters for Robust Affordance Estimation

2026-03-16

AI Summary

This work addresses the challenge of affordance estimation for robots operating under perceptual ambiguities in visual, geometric, and semantic cues. To this end, we propose a novel dual-coupled recursive particle filter framework that explicitly models graspable and movable regions while promoting consensus through bidirectional information exchange. Our approach is the first to introduce coupled particle filters into affordance estimation, integrating recursive Bayesian inference with multimodal perception fusion to significantly enhance robustness and accuracy in challenging conditions such as low illumination and cluttered scenes. Evaluated on real-world datasets, the method achieves a 308%, 245%, and 257% improvement in precision over Where2Act, Hands-as-Probes, and HRP, respectively, and demonstrates a 70% success rate in physical robotic tasks.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Robotic affordance estimation is challenging due to visual, geometric, and semantic ambiguities in sensory input. We propose a method that disambiguates these signals using two coupled recursive estimators for sub-aspects of affordances: graspable and movable regions. Each estimator encodes property-specific regularities to reduce uncertainty, while their coupling enables bidirectional information exchange that focuses attention on regions where both agree, i.e., affordances. Evaluated on a real-world dataset, our method outperforms three recent affordance estimators (Where2Act, Hands-as-Probes, and HRP) by 308%, 245%, and 257% in precision, and remains robust under challenging conditions such as low light or cluttered environments. Furthermore, our method achieves a 70% success rate in our real-world evaluation. These results demonstrate that coupling complementary estimators yields precise, robust, and embodiment-appropriate affordance predictions.

Problem

• affordance estimation
• sensory ambiguity
• robotic perception
• graspable regions
• movable regions

Innovation

• coupled particle filters
• affordance estimation
• recursive estimators
• robust perception
• embodied intelligence