用手套式触觉设备捕捉人与物交互力,让机器人更自然地搬运大件物品。
WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning

- 用可穿戴触觉装置同步采集人体动作与接触力数据,实现力感知的全身操控。
- 在五类复杂任务中,成功率提升且接触位置误差降低,优于四种基线模型。
- 适合研究机器人协作、柔性物体操作及力控策略的科研人员使用。
全身人形机器人操控大型、易变形及需多人协同的物体,需要分布式的接触传感和显式的力调控,但现有模仿学习策略大多仅隐式处理接触力。人类示范能自然体现接触力,但难以直接转化为机器人动作;远程操控虽记录了可执行动作,但力调节不够自然。本文提出WT-UMI,一种可穿戴的全身触觉接口,可部署于人类操作者或人形机器人上,在人类示范与机器人远程操控模式下均能准确获取触觉图像、接触力与末端位姿。我们引入力条件目标位姿修正模块,通过远程操控数据学习将人体姿态转换为具接触意识的机器人目标。为利用人类数据中的自然力交互,提出力监督规划器,预测末端位姿片段与接触力轨迹,该力作为基于触觉的阻抗控制器的参考。在涵盖易变形物体、大型刚性物体及人机协作的五项接触密集型任务中,WT-UMI在成功率与接触位置跟踪误差方面均优于四个基线策略。
原文摘要 · Abstract (English)
Whole-body humanoid manipulation of bulky, deformable, and shared-load objects requires distributed contact sensing and explicit force regulation, yet most imitation policies treat contact force only implicitly. On the other hand, different demonstration sources provide complementary modalities with inherent trade-offs: human demonstrations capture natural contact forces but not robot-executable actions, while teleoperation directly records robot actions but with less natural force regulation. This paper presents \textbf{WT-UMI}, a wearable whole-body tactile interface worn by human operators or mounted on humanoids, providing accurate observations of tactile images, contact forces, and end-effector poses across both human demonstration and humanoid teleoperation modes. We introduce a force-conditioned target-pose correction module that converts measured human poses into contact-aware robot targets by learning corrections from teleoperation data. To leverage the natural force interaction in human data, we propose a force-supervised planner that predicts end-effector pose chunks and contact-force trajectories. The predicted contact force serves as the reference for a tactile-based admittance controller. Across five contact-rich tasks spanning deformable objects, bulky rigid objects, and human--humanoid collaboration, WT-UMI improves success rate and reduces contact-position tracking error over four policy baselines. Our project page is available at https://wt-umi.github.io/WTUMI/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。