用惯性手环实时操控人形机器人，无需学习、低延迟。
Real-Time Whole-Body Teleoperation of a Humanoid Robot Using IMU-Based Motion Capture with Sim2Sim and Sim2Real Validation
- 用惯性传感器捕捉人体动作，直接映射到人形机器人
- 实测可稳定复现行走、鞠躬等20种全身动作，延迟极低
- 适合需要快速部署的机器人远程操控场景
人形机器人全身实时遥操作仍是开放挑战，受限于人体与机器人构型差异、惯性传感器噪声累积、控制延迟以及仿真到现实的迁移鸿沟。本文提出一套完整的实时全身遥操作系统，将佩戴Virdyn惯性动捕服的人体动作直接映射至Unitree G1人形机器人。设计了定制化的运动处理、运动重定向与控制流水线，支持连续低延迟运行，无需离线缓冲或基于学习的组件。系统首先在MuJoCo物理引擎中对Unitree G1模型进行仿真验证（sim2sim），随后未做修改地部署至真实平台（sim2real）。实验结果表明，系统能稳定同步复现包括行走、站立、坐下、转身、鞠躬及协调的全身表达性动作在内的广泛动作库。本工作建立了基于商用可穿戴动捕硬件的实用、可扩展的人形机器人全身遥操作框架。
原文摘要 · Abstract (English)
Stable, low-latency whole-body teleoperation of humanoid robots is an open research challenge, complicated by kinematic mismatches between human and robot morphologies, accumulated inertial sensor noise, non-trivial control latency, and persistent sim-to-real transfer gaps. This paper presents a complete real-time whole-body teleoperation system that maps human motion, recorded with a Virdyn IMU-based full-body motion capture suit, directly onto a Unitree G1 humanoid robot. We introduce a custom motion-processing, kinematic retargeting, and control pipeline engineered for continuous, low-latency operation without any offline buffering or learning-based components. The system is first validated in simulation using the MuJoCo physics model of the Unitree G1 (sim2sim), and then deployed without modification on the physical platform (sim2real). Experimental results demonstrate stable, synchronized reproduction of a broad motion repertoire, including walking, standing, sitting, turning, bowing, and coordinated expressive full-body gestures. This work establishes a practical, scalable framework for whole-body humanoid teleoperation using commodity wearable motion capture hardware.
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