用触觉+视觉融合，实现夹持中物体微动的精准追踪与补偿。
TacSE3: Equivariant SE(3) Motion Estimation from Low-Texture Visuotactile Images for In-Gripper Tracking and Compensation
- 通过接触中心运动和剪切响应解耦三维位姿变化
- 双传感器降低平移与旋转混淆，支持多轴旋转追踪
- 无需重训策略，轻量补偿提升操作抗干扰能力
机器人夹持操作需在频繁视觉遮挡下可靠追踪物体运动，但低纹理触觉-视觉图像难以提供稳定对应点，传统图像或几何匹配方法失效。本文提出TacSE3，一种触觉运动估计流程，将低纹理触觉-视觉观测转换为解耦的三维力场，并在SE(3)上估计刚体增量运动。方法通过接触中心运动推导平面平移，主要利用剪切相关触觉响应估计旋转，获得具有物理可解释性的夹持内追踪信号。使用配对的DM-Tac指端传感器实验表明，双传感器感知有效降低平移-旋转模糊性，支持跨轴及不同物体几何的旋转追踪，并提供轻量级补偿信号，在不重训基础策略的前提下，提升下游操作任务的扰动容错能力。
原文摘要 · Abstract (English)
Robotic in-hand manipulation requires reliable object-motion tracking under frequent visual occlusion, yet low-texture visuotactile images provide few stable correspondences for conventional image- or geometry-matching methods. This paper presents TacSE3, a tactile motion-estimation pipeline that converts low-texture visuotactile observations into a decoupled three-dimensional force field and estimates incremental rigid-body motion on SE(3). The method derives planar translation from contact-centroid motion and estimates rotation primarily from shear-related tactile responses, yielding a physically interpretable signal for in-gripper tracking and compensation. Experiments with paired DM-Tac fingertip sensors show that dual-sensor sensing reduces translation-rotation ambiguity, supports rotation tracking across axes and object geometries, and provides a lightweight compensation signal that improves disturbance tolerance in downstream manipulation tasks without retraining the base policy.
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