Diffusion-based Inverse Observation Model for Artificial Skin

2025-06-16

AI Summary

In tactile inverse problems for artificial skin—where object pose estimation suffers from discontinuous observations, high ambiguity, and stringent contact constraints—this paper introduces the first diffusion-model-based framework. Our method explicitly models observational ambiguity and multimodal uncertainty via conditional denoising training and distributed tactile signal encoding, while integrating simulation-driven physical contact constraints to generate high-fidelity, diverse pose hypotheses. Compared to conventional sampling approaches, our model achieves a 3.2× improvement in hypothesis sampling efficiency and reduces average pose estimation error by 41.7% in simulation. This work provides the first empirical validation of diffusion models’ effectiveness and superiority in modeling physically grounded, multi-solution constraints inherent to tactile inverse problems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Estimating object pose from ambiguous tactile observations
• Sampling valid hypotheses under contact constraints
• Learning inverse observation model using diffusion models

Innovation

• Diffusion models for multimodal distributions
• Inverse observation model with tactile data
• Efficient sampling of contact hypotheses