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arXiv · 2506.13986

Diffusion-based Inverse Observation Model for Artificial Skin

Ante Maric·Julius Jankowski·Giammarco Caroleo·Alessandro Albini·Perla Maiolino·Sylvain Calinon
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Abstract

Contact-based estimation of object pose is challenging due to discontinuities and ambiguous observations that can correspond to multiple possible system states. This multimodality makes it difficult to efficiently sample valid hypotheses while respecting contact constraints. Diffusion models can learn to generate samples from such multimodal probability distributions through denoising algorithms. We leverage these probabilistic modeling capabilities to learn an inverse observation model conditioned on tactile measurements acquired from a distributed artificial skin. We present simulated experiments demonstrating efficient sampling of contact hypotheses for object pose estimation through touch.

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Ante Maric, Julius Jankowski, Giammarco Caroleo, Alessandro Albini, Perla Maiolino, Sylvain Calinon. 2025-06-16. Diffusion-based Inverse Observation Model for Artificial Skin. https://arxiv.org/abs/2506.13986

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