2025 年世界机器人大会期间，亦庄机器人产业园的机器人研究院聚集了大量观众。除了餐厅机器人服务员在 现场服务，机器人乐队的现场演奏更是让其成为餐厅里最亮眼的部分，键盘手机器人指尖在琴键上飞速跳跃， 吉他手机器人的拨弦动作流畅自然，整个乐队配合默契，奏响的乐曲既饱含科技感又充满感染力。那些曾经仅 存于科幻场景下的机器人乐队，如今正逐步走进现实。 ▍ 机械臂弹扬琴、灵巧手敲架子鼓！第三代和璇机器人乐队成 2025WRC显眼包 今年 8月 在北京举行的 2025 世界机器人大会上，来自杭州的第三代和璇机器人乐队成为全场焦点。在机器 人城市会客厅的体验空间内，三位机器人乐手分别执掌钢琴、扬琴与架子鼓，默契合奏《青花瓷》等曲目，悠 扬旋律与精准演奏吸引众多观众驻足鼓掌，更获央视新闻频道《新闻 1+1》等主流媒体重点关注。 这 个备受瞩目的机器人乐队 ， 来自 杭州海创人形机器人创新中心。该创新中心由浙江大学与杭州市余杭区 联合共建，与浙江大学机器人研究院实行 "两块牌子、一套班子" 运营模式，自 2024 年 8 月揭牌以来，便 以场景智能为核心推进技术研发与产业化落地。此次亮相的第三代和璇机器人，正是其科艺融合探索 ...

Core Viewpoint - The article discusses the development of a comprehensive robotic learning suite, Astribot Suite, aimed at enabling robots to perform a wide range of daily tasks through human-like interaction and learning from the environment [3][4]. Group 1: Challenges in Robotic Control - Achieving full-body autonomous control in robots faces three main challenges: designing safe and capable hardware, developing intuitive data collection systems, and creating efficient algorithms for learning from human demonstrations [6]. - A unified framework is proposed to address these challenges, consisting of a high-performance robot platform, a full-body teleoperation system, and a full-body visual-motor strategy [6]. Group 2: High-Performance Robot Platform - The robot platform is designed to be high-performance, durable, and capable of safe mobile operations, utilizing an innovative rope-driven design that mimics human muscle tissue for precise movement and force application [7]. - The design features a lightweight structure, low friction transmission, and soft cushioning, enabling high-resolution force control essential for AI-driven tasks [7]. Group 3: Full-Body Teleoperation - An intuitive and cost-effective teleoperation system is introduced, consisting of a VR headset and handheld joystick, allowing non-experts to efficiently collect data for various tasks [9]. - The system supports first-person and third-person control modes, optimized for different types of tasks with low transmission latency [9]. Group 4: Full-Body Motion Operation Model (DuoCore-WB) - DuoCore-WB is a simple yet effective imitation learning algorithm designed to simulate full-body actions, emphasizing RGB-based visual perception and real-time trajectory generation [10][12]. - The model demonstrates an average success rate of 80% across various tasks, with a peak success rate of 100%, indicating its effectiveness in real-world applications [12]. Group 5: Evaluation of Astribot Suite - Astribot Suite was evaluated on six representative real-world tasks, including delivering drinks, storing cat food, throwing away trash, organizing shoes, throwing toys, and picking up toys, showcasing its capabilities in complex coordination and dynamic stability [12][23]. - The success rates for these tasks varied, with detailed performance metrics provided for each subtask, highlighting the system's robustness and adaptability [23]. Group 6: Key Findings on Motion Representation - The use of end-effector (EE) space action representation reduces error accumulation and enhances task performance compared to joint space representation [25]. - Incremental action representation improves trajectory smoothness and execution stability, particularly in high-frequency control scenarios [25]. - The relative trajectory representation based on the end-effector self-coordinate system enhances visual-action alignment and generalization capabilities [28].

Core Viewpoint - Jinqiu Capital led the A-round financing for Stardust Intelligence in 2024, focusing on long-term investment in groundbreaking AI startups with innovative business models [1]. Group 1: Company Overview - Stardust Intelligence was founded in 2022 by members from Tencent Robotics X, with its name derived from the Latin phrase "Ad astra per aspera," meaning "through difficulties to the stars" [4]. - The company has developed a humanoid robot named Astribot S1, designed to assist with household tasks such as taking out the trash and organizing shoes [4][6]. Group 2: Technological Highlights - The design of Astribot S1 addresses three core challenges in creating a truly general-purpose intelligent robot: body design, data collection, and learning algorithms [6][8]. - The robot features a humanoid structure with seven degrees of freedom in its arms, a height of approximately 1.7 meters, and the ability to lift up to 5 kilograms [10]. - The innovative "cable-driven" technology allows for high-resolution force control and enhanced load capacity compared to traditional rigid structures [11]. Group 3: Learning and Operation Systems - Stardust Intelligence has developed a low-cost, intuitive remote operation system that allows users to teach the robot using common VR devices, with a total cost of under $300 [13]. - The DuoCore-WB learning algorithm enables the robot to learn from human demonstrations, focusing on end-effector space rather than joint angles, improving precision and efficiency [19][22]. - The system operates with a low latency of 20ms for command response, ensuring smooth interaction between the operator and the robot [13][15]. Group 4: Performance and Applications - The robot has been tested on six common household tasks, achieving an average success rate of around 80%, with some tasks reaching 100% success [29][43]. - Specific tasks include delivering drinks, storing cat food, and cleaning up toys, showcasing the robot's ability to perform complex, coordinated actions in various environments [32][36][42]. Group 5: Future Prospects - The Astribot Suite integrates innovative hardware, intuitive control systems, and efficient learning algorithms, marking significant progress toward general-purpose intelligent robots [44]. - Future plans include further advancements in hardware, human-robot interaction, and model algorithms to enhance real-world applications of robotic technology [47].