ViHaTeleop：一种用于灵巧操作学习的低成本轻量型视觉-触觉遥操作系统
ViHaTeleop: A Low-Cost, Lightweight Visual-Haptic Teleoperation System for Dexterous Manipulation Learning
- Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ViHaTeleop是一款低成本轻量型视觉-触觉遥操作系统，结合SLAM、相机跟踪与LRA反馈，经实验验证其可提升接触关键任务的操作成功率与主观体验，支持从演示到策略训练的完整流程。
AI中文摘要：
从演示中学习是灵巧操作的一种有前景的方法，但使用低成本遥操作硬件收集对接触关键的高质量演示仍然困难。我们提出ViHaTeleop，这是一款轻量型（0.7千克）、低成本（550美元）的视觉-触觉遥操作系统，具备基于SLAM的手腕跟踪、基于相机的手部跟踪，以及通过线性谐振器（LRA）实现的逐指振动反馈。该系统包含多项设计选择（LED照明、鱼眼手部相机、触觉感知重定向约束），并在Franka + LEAP Hand + 9DTact的组合上部署于真实和模拟环境中。在匹配的有/无触觉条件下，九名参与者完成六项对接触关键的任务，触觉在所有任务中提高了成功率（提升2.2至15.6个百分点），而完成时间的影响取决于任务。主观评分显示，在模拟和真实环境中，接触清晰度和抓取信心均显著提升（Wilcoxon符号秩检验，p<0.05）。我们还在Isaac Sim中集成了轻量型深度相机触觉代理，实现从多模态演示收集到视觉-触觉策略训练的完整流程。通过从收集的演示中训练视觉-触觉策略进行的初步下游验证显示，触觉线索有益于对接触关键的子任务（插针任务：相比仅视觉提升17个百分点）。
英文摘要：
Learning from demonstration is a promising approach for dexterous manipulation, but collecting high-quality contact-critical demonstrations remains difficult with low-cost teleoperation hardware. We present ViHaTeleop, a lightweight (0.7 kg), low-cost (\$550) visual-haptic teleoperation system with SLAM-based wrist tracking, camera-based hand tracking, and finger-wise vibrotactile feedback through Linear Resonant Actuators (LRA). The system includes several design choices (LED illumination, fisheye hand camera, and tactile-aware retargeting constraints) and is deployed on Franka + LEAP Hand + 9DTact in both real and simulated environments. Under matched with/without-haptic conditions with nine participants across six contact-critical tasks, haptics improved success rates across all tasks (+2.2 to +15.6 percentage points), while completion-time effects were task-dependent. Subjective ratings showed significant gains in contact clarity and grasp confidence in both simulation and real-world settings (Wilcoxon signed-rank, $p<0.05$). We also integrate a lightweight depth-camera-based tactile proxy in Isaac Sim, enabling a full pipeline from multi-modal demonstration collection to visual-tactile policy training. Preliminary downstream validation by training visual-tactile policies from collected demonstrations shows tactile cues benefit contact-critical subtasks (peg-in-hole: +17 percentage points over vision-only).
