Teleopit：一种全 embodiment 人形遥操作系统
Teleopit: A Full-Embodiment Humanoid Teleoperation System
- Westlake University（西湖大学）
- Shanghai Innovation Institute（上海创新研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出 Teleopit 全 embodiment 人形遥操作系统，结合改进的动作跟踪器与优化手部重定向器，收集 96 次演示训练的 ACT、GR00T N1.7 策略，在人形机器人上任务成功率达 90.0%、95.0%。
AI中文摘要：
用于演示收集的人形遥操作需要协调的全身运动、连续的灵巧手控制以及视点控制。现有系统要么简化手部命令，要么依赖专用可穿戴传感器实现细粒度手部运动。我们提出 Teleopit，一种全 embodiment 遥操作系统，可将来自 VR 的身体、手部和头部信号映射到人形本体、可配置灵巧手以及 2 自由度主动视觉模块。历史编码器和感知故障的回退采样可在动作捕捉和实时 VR 参考下改进动作跟踪器。基于优化的手部重定向器结合归一化手指方向、指尖闭合和拇指框架对齐，可将人手动作映射到不同灵巧手，无需调整特定于手的目标或求解器超参数。组件实验评估跟踪成功率和重定向行为，而真实机器人遥操作展示了协调的移动、操作和视点控制。在使用 Teleopit 收集的 96 次成功演示上训练的 ACT 和 GR00T N1.7 策略，部署在人形机器人上时任务成功率分别达到 90.0% 和 95.0%。项目页面可访问此 https URL。
英文摘要：
Humanoid teleoperation for demonstration collection requires coordinated whole-body motion, continuous dexterous hand control, and viewpoint control. Existing systems either simplify hand commands or depend on dedicated wearable sensors for fine-grained hand motion. We introduce Teleopit, a full-embodiment teleoperation system that maps body, hand, and head signals from VR to a humanoid body, configurable dexterous hands, and a 2-DoF active vision module. A history encoder and failure-aware rewind sampling improve the motion tracker on both motion-capture and live VR references. An optimization-based hand retargeter combines normalized finger directions, fingertip closure, and thumb-frame alignment to map human hand motion to different dexterous hands without tuning hand-specific objective or solver hyperparameters. Component experiments evaluate tracking success rate and retargeting behavior, while real-robot teleoperation demonstrates coordinated locomotion, manipulation, and viewpoint control. ACT and GR00T N1.7 policies trained on 96 successful demonstrations collected with Teleopit achieve task success rates of 90.0% and 95.0%, respectively, when deployed on the humanoid. The project page is available at https://botrunner64.github.io/teleopit-page.
