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活力中国调研行丨北京:动作捕捉技术推动智能体育竞技发展
Yang Guang Wang· 2025-06-21 02:38
Core Insights - A Beijing-based company specializing in motion capture technology for the film and sports health industries has expanded its business to train humanoid robots in football since last year, showcasing the intersection of technology and entertainment [1] - The development of motion capture equipment, initially designed for sci-fi films, has found new applications in robotics, leading to increased demand for motion data [1] - A robot football league is set to take place on June 28 in Beijing, where identical robots will compete under different software configurations, emphasizing the importance of algorithm development [2] Industry Developments - The upcoming World Humanoid Robot Sports Competition in August at Beijing's Bird's Nest will be the first comprehensive competition for humanoid robots globally, highlighting the growing interest in robotics [2] - The advancements in humanoid robotics are expected to inspire new ideas across various sectors, including youth engagement and capital markets, indicating a significant opportunity for innovation [2]
他们的动作捕捉技术让机器人能看会学
Bei Jing Qing Nian Bao· 2025-06-09 17:51
Core Viewpoint - The article highlights the innovative advancements made by the youth technology team, Yuan Ke Shi Jie, in developing a motion capture system, FZMotion, which enhances the capabilities of robots by enabling them to learn human movements and apply them in various practical scenarios [3][4][5]. Group 1: Technology Development - Yuan Ke Shi Jie has developed FZMotion, a motion capture system that accurately records human movements and converts them into data for robots to learn [3][4]. - The team has successfully implemented FZMotion in various applications, including training for the national boxing team and performances at the 2025 CCTV Spring Festival Gala [3][4]. - The technology features a "skeleton correlation algorithm" that allows it to infer true motion trajectories even when some data is obscured, significantly improving robot performance in complex environments [3][4]. Group 2: Collaboration and Innovation - The company collaborates with several universities, including Tsinghua University and Beijing Institute of Technology, to integrate academic research with practical applications [4][5]. - Yuan Ke Shi Jie has established a long-term industry-academia-research platform, facilitating the transition of academic findings into real-world applications [4][5]. - The team emphasizes a collaborative work environment, encouraging equal participation and idea sharing among all members, regardless of their position [4][5]. Group 3: Market Impact and Future Prospects - The advancements in motion capture technology are positioned to expand into various fields, including autonomous vehicles and underwater robotics, indicating a broad market potential [3][5]. - The recent policy initiatives from Beijing aim to establish the city as a global hub for embodied intelligence innovation, creating new opportunities for companies like Yuan Ke Shi Jie [5]. - The team's success in competitions and projects reflects their ability to adapt and thrive in a rapidly evolving technological landscape, showcasing the potential of youth-led innovation [4][5].
机器人动捕设备专家
2025-05-20 15:24
Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the robotics motion capture industry, focusing on data collection methods and challenges faced by companies in this sector [1][2][4]. Core Insights and Arguments - **Data Collection Modes**: There are four primary modes of data collection in motion capture systems: 1. Real human motion capture with a physical robot, yielding 30% to 50% effective data but at a high cost. 2. Combination of real motion capture and virtual engines, allowing for 15 to 20 minutes of data collection per day at a lower cost. 3. Pure motion capture systems without physical robots, resulting in a lower effective data ratio. 4. Use of synthetic data for large-scale training, which is currently debated [2][19]. - **Data Validity Measurement**: Validity is assessed through initial human motion verification followed by robot posture validation. There is no industry standard, and the process involves multi-sensor information fusion to ensure reliability [5]. - **Data Collection Efficiency**: The efficiency of data collection is low, with 1,300 seconds of data requiring experienced motion capture experts to work continuously for several days. The main issues are the immaturity of virtual body software and challenges in interacting with real objects [6][3]. - **Cost of Data Collection**: The cost of effective data collection is approximately 300 yuan per second, with repeated data costing around 60 yuan per second. Future projections suggest costs may drop to around 200 yuan in 1-3 years, potentially below 100 yuan with student involvement [3][22]. - **Mapping Challenges**: The primary challenge in motion capture technology is the mapping of human actions to robotic actions. Current solutions often prioritize accuracy over posture, which can lead to discrepancies in execution [7][9]. - **Role of Data Factories**: Establishing data factories can significantly enhance data collection efficiency, allowing for the use of hundreds to thousands of devices to gather extensive data, which is crucial for training algorithms [10]. - **Customer Demand**: The most significant current demand comes from companies like Shiyuan, which has placed a large order of 1,000 sets, while most other companies remain in the verification stage [16]. Other Important but Overlooked Content - **Application Prioritization**: Data collection priorities are determined by customer needs and application scenarios rather than specific actions [11][12]. - **Domestic Companies' Focus**: Major domestic companies are concentrating on data collection in areas such as home services, healthcare, and rescue applications, tailoring their data collection environments accordingly [13]. - **Integration of Data Types**: The integration of motion capture data with force and tactile information is being explored to enhance the capabilities of motion capture devices [18]. - **Challenges in Mapping Solutions**: Companies face challenges in understanding human biomechanics when designing mapping solutions, often outsourcing this work to specialized motion capture firms [25]. - **Future Cost Reduction Strategies**: Cost reduction in data collection can be achieved through bulk production and collaboration with educational institutions to utilize student labor [21].
详解机器人动作捕捉技术
2025-03-16 14:53
Summary of Motion Capture Technology in Robotics Industry Overview - The motion capture technology is divided into two main categories: inertial and optical systems. Inertial systems are portable but susceptible to interference, while optical systems offer high precision but come with higher costs [2][4][5]. Key Points and Arguments - **Inertial Motion Capture**: - Utilizes multi-axis sensors (e.g., nine-axis or twelve-axis) to collect motion data. It is portable and easy to operate, but suffers from cumulative errors and interference from magnetic fields [4][5][8]. - Data accuracy can be compromised in environments with Bluetooth or WiFi interference, leading to pose estimation errors and data loss [5][9]. - **Optical Motion Capture**: - Achieves sub-millimeter precision and is not affected by magnetic fields, making it suitable for high-precision applications. However, it requires a controlled environment and is sensitive to physical vibrations and lighting conditions [2][7][8]. - The cost of optical systems can be significant, with setups costing around 300,000 to 400,000 RMB [4]. - **Market Segmentation**: - The motion capture market is segmented into three tiers: - High-end market dominated by Yobeat and OptiTrack, serving major clients like Tencent and Alibaba. - Mid-tier market catering to general industrial applications and research institutions. - Niche market served by smaller companies providing specialized solutions [2][10][12]. - **Data Quality and Processing**: - Optical motion capture systems yield high data quality with an output rate of 98%-99%, while inertial systems have a slightly lower output rate of 90%-95%. Optical data often requires further processing, while robotic motion capture data is generally more straightforward [18][24]. - **Application Trends**: - The application of motion capture technology in robotics is expanding, particularly in embodied intelligence and model training. Leading companies in China are actively developing motion capture algorithms for humanoid robots [3][19][20]. Additional Important Insights - **Industry Competition**: - The competitive landscape is characterized by established players like Yobeat and OptiTrack, which have a strong presence in high-end markets, while newer entrants are gradually capturing mid-tier segments [10][13]. - **Commercial Models**: - Companies are encouraged to establish dedicated motion capture environments to enhance data collection efficiency and client satisfaction, which in turn improves profitability [14][15]. - **Future Trends**: - Data collection will become increasingly essential in robotics development, driven by the need for extensive motion data to train models effectively. This trend is expected to support the growth of the industry [21][22]. - **Optical vs. Inertial Systems**: - The future of motion capture in China is likely to remain dominated by optical systems due to their superior accuracy and lower operational costs compared to inertial systems [23][24]. This summary encapsulates the critical aspects of motion capture technology in the robotics sector, highlighting its current state, competitive dynamics, and future trends.