Teleopit: A Full-Embodiment Humanoid Teleoperation System
2026-08-03 • Robotics
Robotics
AI summaryⓘ
The authors present Teleopit, a system that lets people control a humanoid robot using VR by capturing full-body, hand, and head movements. Teleopit improves tracking accuracy with special techniques and can adapt human hand motions to different robot hands without complex tuning. Their tests show good success in tracking and hand control, and real-use trials demonstrate smooth walking, manipulation, and viewpoint adjustments. Policies trained on demonstrations collected via Teleopit achieve high task success rates on the robot.
humanoid teleoperationVR motion trackingdexterous hand controlmotion retargetingactive visionoptimization-based mappingteleoperation demoslocomotion and manipulationfailure-aware rewind samplinghistory encoder
Authors
Bingqian Wu, Zicheng Xu, Xianghui Fan, Dayu Li, Xiangru Huang
Abstract
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.