具身智能数采方案：全身动捕工作一览

Core Viewpoint - The article discusses various advanced full-body motion capture solutions in the robotics industry, highlighting their technical complexities and potential applications in humanoid robot control [1][22]. Group 1: OpenWBC - OpenWBC project enables full-body control of the Unitree G1 robot using Apple Vision Pro for upper body teleoperation and OpenHomie algorithm for lower body movement, supporting full-body data collection [3]. Group 2: TWIST - TWIST is a teleoperated whole-body imitation system developed by Stanford University, allowing remote control of humanoid robots with a focus on coordinated full-body actions, real-time control, and modular design [4][5]. - The system utilizes human motion capture data to enhance tracking accuracy and enables complex movements through a single neural network controller [5]. Group 3: AMO - AMO, developed by UC San Diego, combines reinforcement learning and trajectory optimization for real-time adaptive full-body control in humanoid robots, addressing challenges related to high degrees of freedom and nonlinear dynamics [8][10]. - The framework demonstrates superior stability and expanded workspace capabilities compared to baseline methods, validating its robustness through real-world task execution [10]. Group 4: R²S² Framework - The R²S² framework from Tsinghua University and Galaxy General focuses on enabling humanoid robots to achieve extensive reachability through coordinated control of various skills, ensuring optimal performance and robust transferability from simulation to reality [15]. Group 5: CLONE - CLONE, developed by Beijing Institute of Technology, introduces a closed-loop error correction system for humanoid robot teleoperation, achieving unprecedented fidelity in full-body operations while minimizing positional drift [19]. Group 6: Community and Resources - The article promotes a community platform for knowledge exchange in embodied intelligence, offering resources such as academic content, job information, and technical routes for both beginners and experienced researchers [22][25][31].