发表机构
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology; Electric Power Research Institute, Guangdong Power Grid Co Ltd.（华中科技大学机械科学与工程学院智能制造装备与技术国家重点实验室; 广东电网有限责任公司电力科学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对仅触觉操作的手中6自由度物体位姿精化难题，提出基于SE(3)的物理信息滑动窗口粒子滤波，结合多触觉帧融合等技术，在Allegro Hand V5实验中取得优于基线的位姿精化效果。
AI 中文摘要
本文研究在视觉不可用或严重遮挡的静态及短准静态手中构型下，仅依靠触觉实现被抓物体的6自由度位姿精化与置信度维护。核心难点在于触觉的部分可观测性：全手触觉传感器（taxel）接触稀疏、间断，且在有限激励与物体对称性下存在歧义。我们提出一种基于SE(3)的物理信息粒子滤波器，通过密集全手触觉测量更新位姿置信度。似然函数结合主动接触符号距离一致性、力-法向对齐、摩擦锥可行性、零力负证据及可选可行性保护。滑动窗口对数似然融合近期触觉帧以减少单帧歧义，而势场引导的提议分布引导粒子远离手-物体穿透。感知对称性的重采样保留多个合理模式。在Allegro Hand V5上对5个物体的实验表明，与仅触觉几何、粒子滤波及学习基线相比，本文方法的归一化ADD-S更低； ablation实验证实了时间融合、势场引导及模式保留的益处。
英文摘要
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.
CommentsAccepted by IEEE RAL journal
