Astribot Suite：面向多样化真实环境、聚焦全身操作的框架

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].