Core Viewpoint - The article discusses the development of a general-purpose intelligent robot, emphasizing the importance of mimicking human evolution through continuous interaction with the environment and learning from human behavior, while addressing challenges in hardware design, intuitive data collection interfaces, and learning algorithms [4][7]. Group 1: Introduction and Challenges - The goal of creating intelligent robots that can coexist with humans and assist in daily life has been a long-standing vision, requiring learning from fine interactions with the physical world [7]. - Three fundamental challenges are identified: designing safe and capable robot hardware, developing intuitive data collection interfaces, and creating learning models that can handle the complexity of whole-body control [7][8]. Group 2: Astribot Suite Overview - The Astribot Suite is introduced as a unified framework to address the challenges of whole-body manipulation, consisting of a high-performance robot platform, an intuitive remote operation interface, and a learning algorithm for whole-body visual-motion strategies [4][28]. - The robot platform, Astribot S1, features dual 7-degree-of-freedom arms, a flexible torso, and a mobile base designed for high mobility and accessibility in daily tasks [10][12]. Group 3: Hardware Components - The Astribot S1 robot is equipped with various onboard sensors for robust scene understanding and manipulation, including RGB cameras and LiDAR for spatial perception [12][13]. - The remote operation system utilizes a Meta Quest 3S VR headset for intuitive control, allowing operators to perform tasks with high precision and low latency [14][16]. Group 4: Learning Methodology - The DuoCore-WB algorithm is presented as a simple yet effective method for learning coordinated whole-body actions from demonstration data, emphasizing compatibility with large-scale pre-training [17][19]. - The algorithm utilizes a transformer-based model to learn actions in the end-effector space, reducing error accumulation and enhancing robustness to large viewpoint changes [19][21]. Group 5: Experimental Analysis - The effectiveness of the Astribot Suite is evaluated through six representative tasks, demonstrating an average success rate of 80% for the DuoCore-WB algorithm, with the highest success rate reaching 100% [26][27]. - The remote operation interface is shown to be efficient and intuitive, allowing users to generate smooth and accurate robot actions with a high replay success rate [25][26]. Group 6: Future Directions - Future plans include enhancing robot hardware for improved capabilities and safety, iterating on more intuitive human-robot interaction methods, and optimizing model and system scalability for broader deployment [28].