低成本实时捕捉手部动作，支持机器人远程操作与视觉语言动作数据构建
MirrorLimb: Implementing hand pose acquisition and robot teleoperation based on RealMirror
- 基于PICO的框架实现低成本手部姿态实时采集
- 在成本和精度上优于主流视觉追踪与动捕方案
- 兼容RealMirror生态，适合机器人操控与多模态研究
本文提出一种基于PICO的机器人远程操作框架，可实现低成本、实时的手部运动与姿态数据采集，在成本效益上优于主流视觉追踪与动捕方案。该框架原生兼容RealMirror生态系统，可在Isaac仿真环境中稳定精准地记录机器人轨迹，助力构建视觉-语言-动作（VLA）数据集。系统支持多种末端执行器装备机器人的实时遥操作，包括灵巧手与机械夹爪。本工作旨在降低上肢机器人操作研究的技术门槛，推动VLA相关领域的发展。
原文摘要 · Abstract (English)
In this work, we present a PICO-based robot remote operating framework that enables low-cost, real-time acquisition of hand motion and pose data, outperforming mainstream visual tracking and motion capture solutions in terms of cost-effectiveness. The framework is natively compatible with the RealMirror ecosystem, offering ready-to-use functionality for stable and precise robotic trajectory recording within the Isaac simulation environment, thereby facilitating the construction of Vision-Language-Action (VLA) datasets. Additionally, the system supports real-time teleoperation of a variety of end-effector-equipped robots, including dexterous hands and robotic grippers. This work aims to lower the technical barriers in the study of upper-limb robotic manipulation, thereby accelerating advancements in VLA-related research.
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