用触觉信息精修抓取物体6自由度姿态，解决触觉观测不全难题。
Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement
- 基于SE(3)的物理引导粒子滤波，融合密集触觉数据更新姿态信念。
- 在五种物体上实现更低的ADD-S误差，优于几何、滤波与学习基线。
- 适合无视觉或遮挡严重场景，对称物体多模态姿态保持能力强。
本文研究在无视觉或严重遮挡条件下，静态及短时准静态抓握状态下仅依赖触觉进行6自由度姿态精修与信念维持。核心挑战是触觉部分可观测性：全手传感器接触稀疏、间断且受有限激励与物体对称性影响而模糊。提出一种定义于SE(3)的物理信息粒子滤波器，从密集全手触觉测量中更新姿态信念。似然函数结合主动接触有向距离一致性、力法向对齐、摩擦锥可行性、零力负证据及可选可行性约束。滑动窗口对数似然融合近期触觉帧以降低单帧模糊性，势场引导提议机制防止粒子穿透物体。对称性感知重采样保留多个可能模式。在Allegro Hand V5上对五种物体的实验表明，该方法的归一化ADD-S低于触觉仅几何、粒子滤波与学习基线；消融实验证明时间融合、势场引导与模式保持的有效性。
原文摘要 · Abstract (English)
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.
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