Diffusion-based Inverse Observation Model for Artificial Skin

2025/06/16 by Ante Marić, Julius Jankowski, Maric, Ante +9
Materials Science · #FOS: Computer and information sciences #Robotics (cs.RO) #Textile materials and evaluations

paper · pdf · doi:10.48550/arxiv.2506.13986

openalex publication_date 2025/06/16 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

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.

Citations

• Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models
• 6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
• Tac2Pose: Tactile object pose estimation from the first touch
• LEO: Learning Energy-based Models in Factor Graph Optimization
• Denoising Diffusion Probabilistic Models
• Pose estimation for planar contact manipulation with manifold particle filters

Related

• Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
• Tactile Probabilistic Contact Dynamics Estimation of Unknown Objects
• MidasTouch: Monte-Carlo inference over distributions across sliding touch
• Tac2Pose: Tactile object pose estimation from the first touch
• ScoreHOI: Physically Plausible Reconstruction of Human-Object Interaction via Score-Guided Diffusion