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Core Viewpoint - Motion capture technology is a crucial support for humanoid robots, enabling them to learn and assess their movements, thus facilitating their transition from experimental prototypes to industrial products [1][3][24]. Group 1: Role of Motion Capture Technology - Motion capture serves as a "coach" for humanoid robots, helping them establish movement intelligence, which is essential for their basic survival capabilities [3][4]. - The technology aids in training robots to perform tasks like dancing or jumping by capturing human joint movement data and replicating it in robots [3][4]. - Motion capture also plays a role in assessing robots' performance, acting as a "examiner" to ensure safety and efficiency in their movements [7][8]. Group 2: Training and Assessment - The training process involves two main aspects: movement intelligence and operational intelligence, which are further divided into three levels: movement, task, and interaction intelligence [3][4]. - Operational intelligence focuses on the robot's ability to perform tasks, such as using dexterous hands to manipulate objects with precision [4][6]. - Assessment involves evaluating robots' movements against human standards to identify and correct any anomalies, enhancing their performance [7][8]. Group 3: Innovations in Motion Capture - The introduction of the "optical-inertial hybrid motion capture solution" addresses traditional motion capture issues like occlusion and drift, significantly improving accuracy and reliability [9][11]. - This hybrid solution combines high precision from optical capture with the continuity of inertial capture, allowing for real-time calibration and enhanced data quality [11][13]. - The use of active light-emitting markers helps mitigate interference from reflective surfaces, ensuring accurate data collection even in challenging environments [14][16]. Group 4: Data Requirements for Humanoid Robots - Humanoid robots require extensive and high-quality data to function effectively, with data needs surpassing those of other AI applications like ChatGPT and autonomous driving [18][19]. - The establishment of a high-quality humanoid robot dataset is underway, focusing on multi-modal data that includes motion, tactile, environmental, and object property information [18][21]. - Ensuring data accuracy and real-time usability is critical, as traditional data collection methods often fall short in providing the necessary quality for effective robot training [21][22]. Group 5: Future Directions - The future of humanoid robots involves a phased approach, starting with factory settings, progressing to healthcare, and eventually reaching home environments, all supported by motion capture technology [24]. - As motion capture technology evolves, it is expected to transform humanoid robots from mere tools into educators, capable of teaching humans through precise demonstrations of tasks [22][24].