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].

Market Performance - On May 14, 2025, the CSI 300 index rose by 1.21%, while the machinery sector declined by 0.43%, ranking 29th among all primary industries [2][1] - Within the sub-sectors, semiconductor equipment had the highest increase of 0.79%, whereas engineering machinery experienced the largest drop of 1.96% [2][1] - The top three gainers in individual stocks were Heng Er Da (+20.00%), Zhong Ji Huan Ke (+19.97%), and Da Ye Co. (+12.98%); the top three losers were Magnetic Valley Technology (-8.20%), Xin Yu Ren (-7.46%), and De Ma Technology (-6.19%) [2][1] Company Announcements - New Era's shareholder Wang Chunxiang plans to reduce his stake by 0.15% through block trading or centralized bidding, having previously held 2.12% [3] - Guangge Technology's major shareholder Beijing Jishi Chuangye Investment Fund reduced its stake by 0.27% from 5.00% between May 7 and May 13, 2025 [3] - Fengxing Co.'s major shareholder Jiangxi Taihao Technology Development Co. has reduced its stake by 1.02% from 7.92% through centralized bidding [3] - Zhuozhao Point Glue's shareholder Yinghao (Hainan) Venture Capital Co. has reduced its stake by 0.2914% from 1.2230% through centralized bidding [3] Industry News - Zhiyuan Robotics and the University of Hong Kong launched UniVLA, a new framework for universal strategy learning in robotics, which allows for cross-domain, cross-scenario, and cross-task capabilities [6] - UniVLA's core innovation is the task-centric latent action space, enabling efficient learning from vast amounts of unlabeled video data, achieving state-of-the-art performance with significantly lower computational resources [6] - The model demonstrated an average success rate improvement of 18.5% across four evaluation metrics and achieved state-of-the-art results with only 10% of the data in specific tasks [6] - The first practical quantum-resistant chip "Mi Xin PQC01" was released by Zhengzhou Xinda Yimi Technology Co., featuring 100% domestic production and core technology [7][8] - The chip supports dynamic switching between quantum-resistant and classical algorithms, operates on a 28nm process, and reduces power consumption by 60%, making it suitable for IoT and mobile devices [8]