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arXiv · 2603.15223

Coupled Particle Filters for Robust Affordance Estimation

Patrick Lowin·Vito Mengers·Oliver Brock
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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.

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Patrick Lowin, Vito Mengers, Oliver Brock. 2026-03-16. Coupled Particle Filters for Robust Affordance Estimation. https://arxiv.org/abs/2603.15223

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