Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement

2026-08-18

AI Summary

本文针对仅触觉条件下6自由度物体位姿精化问题，提出了一种基于物理信息的滑动窗口粒子滤波方法，通过融合最近触觉帧和考虑对称性来提高位姿估计精度。

Abstract

This paper studies tactile-only 6-DoF pose refinement and belief maintenance for grasped objects in static and short quasi-static in-hand configurations where vision is unavailable or heavily occluded. The key difficulty is tactile partial observability: whole-hand taxel contacts are sparse, intermittent, and ambiguous under limited excitation and object symmetries. We propose a physics-informed particle filter on $\mathrm{SE}(3)$ that updates pose beliefs from dense whole-hand tactile measurements. The likelihood combines active-contact signed-distance consistency, force-normal alignment, friction-cone feasibility, zero-force negative evidence, and optional feasibility guards. A sliding-window log-likelihood fuses recent tactile frames to reduce single-frame ambiguity, while a potential-field-guided proposal steers particles away from hand--object penetration. Symmetry-aware resampling preserves multiple plausible modes. Experiments on an Allegro Hand V5 with five objects show lower normalized ADD-S than tactile-only geometric, particle-filter, and learning baselines, and ablations confirm the benefits of temporal fusion, potential guidance, and mode preservation.

Problem

• tactile-only
• 6-DoF pose refinement
• partial observability
• in-hand manipulation
• object symmetries

Innovation

• physics-informed particle filtering
• sliding-window log-likelihood
• symmetry-aware resampling
• potential-field-guided proposal