# 面向仅触觉手内6-DoF物体位姿精修的物理信息滑动窗口粒子滤波

> 原文链接：https://robotics.hellobike.com/papers/2026-09-02-2608-17601
> 频道：papers | 更新日期：2026-09-02 | 标签：arxiv, cs.RO
> 核心摘要：针对视觉不可用或遮挡下的手内物体位姿精修，提出物理信息滑动窗口粒子滤波，并在Allegro Hand V5五个物体上验证。

# 面向仅触觉手内6-DoF物体位姿精修的物理信息滑动窗口粒子滤波

::: v-pre
> 原文链接：[Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement](https://arxiv.org/abs/2608.17601)（arXiv 2608.17601 · 发布日期：2026-09-02）

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

针对视觉不可用或遮挡下的手内物体位姿精修，提出物理信息滑动窗口粒子滤波，并在Allegro Hand V5五个物体上验证。

## 核心要点

- 视觉不可用或严重遮挡时，稀疏间歇且歧义的触觉接触造成部分可观测难题。
- 在SE(3)上更新位姿信念，似然结合接触距离、法向力、摩擦锥和零力负证据。
- 滑动窗口融合近期触觉帧，势场引导提议避开穿透，对称感知重采样保留多模态。
- 实验显示normalized ADD-S低于几类仅触觉基线，消融验证时间融合等模块收益。

**验证范围与局限**：验证范围为Allegro Hand V5上五个物体的静态和短时准静态手内配置。

## 英文摘要 (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.

作者：Lingjun Shao, Ying Zhang, Xiangfei Li, Xiangyang Li, Huan Zhao, Zhenyu Wang, Han Ding
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## 原始参考来源 (Sources)

- [arXiv 2608.17601：Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement](https://arxiv.org/abs/2608.17601)