Core Viewpoint - The article highlights the rapid development and commercialization of embodied intelligence, emphasizing the competitive landscape among global teams and the importance of technological breakthroughs in the field [4][5]. Group 1: Industry Overview - The last two years have seen significant advancements in hardware, data collection, and algorithms, leading to the expansion of embodied intelligence beyond laboratory settings [4]. - Embodied intelligence has become a recognized core direction for commercialization globally, with various teams competing intensely in this space [4]. - The next generation of technological breakthroughs will focus on general embodied intelligence and scene-adaptive learning [4]. Group 2: Key Players in Embodied Intelligence - **Yushu Technology**: Founded by Wang Xingxing, the company specializes in quadruped robots and has developed multiple models, including Laikago and AlienGo. Wang has over 10 years of experience in robot development and holds over 100 patents [8]. - **Xinghai Map**: Co-founded by Zhao Xing, the company focuses on embodied intelligence and multimodal learning, contributing to the development of the first mass-produced autonomous driving model based on large models [12][13]. - **Galaxy General**: Founded by Wang He, the company is dedicated to embodied intelligence and humanoid robots, with significant research contributions in 3D vision and robot learning [18]. - **Zhiyuan Robotics**: Led by Luo Jianlan, the company focuses on high-precision assembly tasks using reinforcement learning, achieving a 100% success rate in real-world applications [23]. - **Variable Robotics**: Co-founded by Wang Hao, the company aims to integrate large models with embodied intelligence, launching the WALL-A model, which is the largest operational model globally [26]. - **Zhuji Power**: Founded by Zhang Wei, the company is developing full-size humanoid robots and has launched the W1 commercial robot, with plans for mass production of humanoid robots by 2025 [30]. - **Stardust Intelligence**: Founded by Lai Jie, the company focuses on creating AI robots for household use, achieving breakthroughs in embodied intelligence data acquisition [32]. - **Cloud Deep**: Founded by Zhu Qiuguo, the company specializes in humanoid and quadruped robots, with a strong emphasis on self-research and development of core components [34]. - **Qianxun Intelligence**: Founded by Han Fengtao, the company has developed the Moz1 humanoid robot, which features advanced control capabilities and has raised over 1 billion yuan in funding [38]. - **Physical Intelligence**: Co-founded by Sergey Levine, the company focuses on creating advanced AI models for robots, achieving significant funding milestones and technological advancements [40][41]. - **Figure AI**: Founded by Brett Adcock, the company has developed humanoid robots for commercial applications, with significant advancements in collaborative robot control [44][45]. Group 3: Future Outlook - The article concludes that the vision and persistence of technology leaders are crucial for advancing the industry, with various paths being taken towards a flexible, adaptive, and highly interactive future in embodied intelligence [54][55].

Group 1 - The core achievement of Harbin Engineering University is the development of an electro-hydraulic deep-sea soft robot, which has been tested in various deep-sea environments, including depths of 1369 meters and 4070 meters [2] - The robot utilizes a miniaturized energy control system for electro-hydraulic unit coordination, enabling it to perform various movements in deep-sea conditions [2] - The soft robot is equipped with a micro deep-sea optical perception system, allowing it to sense its motion state and environmental targets in extreme underwater conditions [2] Group 2 - Figure Company showcased its humanoid robot, Figure 02, which can autonomously fold clothes, utilizing the Helix model for visual language action [5] - The Helix model allows the robot to learn directly from real-world scenarios without relying on rigid fabric models, enhancing its operational capabilities [5] - The robot's neural network has transitioned from merely moving boxes to accurately picking up clothes and folding them through a data-driven approach [5] Group 3 - The 2025 World Robot Conference in Beijing resulted in significant industry promotion, with over 19,000 robots and related products sold, generating sales exceeding 200 million yuan [9] - The conference featured 220 renowned domestic and international robot companies, showcasing 1569 products and securing a total financing amount of 1.481 billion yuan [9] - New initiatives were launched during the conference, including talent cultivation plans and the release of research reports on the development trends of embodied intelligent robots [9] Group 4 - A new data-driven framework called Bioinspired Predictive Slip Control (BPSC) was proposed to enhance robotic manipulation by actively suppressing slip during operations [12] - The BPSC framework integrates neural network predictions with model predictive control, significantly improving stability and adaptability in complex handling tasks [12] - Experimental results indicate that the slip suppression efficiency of this method is 82% higher than traditional gripping force control methods [12] Group 5 - Ninebot Company reported a 76.14% year-on-year increase in revenue, reaching 11.742 billion yuan, and a net profit growth of 108.45%, amounting to 1.242 billion yuan [15] - The company has accumulated 3,790 patents and is actively involved in standardization activities, filling several industry gaps across various technology fields [15] - Key technologies developed by Ninebot include sensorless drive technology and autonomous navigation systems, which have been widely applied across multiple product lines [15]

Core Viewpoint - The article highlights the advancements in humanoid robots, particularly focusing on Figure's new model, which utilizes reinforcement learning to achieve more natural walking patterns, resembling human movement more closely [3][4][22]. Group 1: Technological Advancements - Figure's new humanoid robot, Figure 02, demonstrates significant improvements in walking, appearing more human-like with a lighter gait and faster speed [4][6]. - The walking control system is trained using reinforcement learning, which allows the robot to learn how to walk like a human through simulated trials [9][14]. - The training process involves high-fidelity physical simulations, enabling the collection of years' worth of data in just a few hours [10][14]. Group 2: Simulation Techniques - The training incorporates domain randomization and high-frequency torque feedback to bridge the gap between simulation and real-world application, allowing the learned strategies to be applied directly to physical robots without additional adjustments [11][18]. - The robots are exposed to various scenarios during training, learning to navigate different terrains and respond to disturbances [15][18]. Group 3: Future Plans and Industry Context - Figure plans to expand this technology to thousands of Figure robots, indicating a significant scaling of their operations [21]. - The article notes a broader trend in the industry, with many companies, including Vivo, launching their own robotics initiatives, reflecting a growing interest in humanoid robots [24][25].