Core Viewpoint - The article discusses the rapid advancements in embodied intelligence, highlighting its potential to revolutionize various industries by enabling robots to understand language, navigate complex environments, and make intelligent decisions [1]. Group 1: Embodied Intelligence Technology - Embodied intelligence aims to integrate AI systems with physical capabilities, allowing them to perceive and interact with the real world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field [1]. - The potential applications of embodied intelligence span manufacturing, healthcare, service industries, and space exploration [1]. Group 2: Technical Challenges - Achieving true embodied intelligence presents unprecedented technical challenges, requiring advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2]. Group 3: Role of MuJoCo - MuJoCo (Multi-Joint dynamics with Contact) is identified as a critical technology for embodied intelligence, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [3]. - It allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without risking expensive hardware [5]. - MuJoCo's advantages include high simulation speed, the ability to test extreme scenarios safely, and effective transfer of learned strategies to real-world applications [5]. Group 4: Research and Industry Adoption - MuJoCo has become a standard tool in both academia and industry, with major companies like Google, OpenAI, and DeepMind utilizing it for robot research [7]. - Mastery of MuJoCo positions entities at the forefront of embodied intelligence technology [7]. Group 5: Practical Training and Curriculum - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations within the embodied intelligence technology stack [9]. - The course includes project-driven learning, covering topics from physical simulation principles to deep reinforcement learning and Sim-to-Real transfer techniques [9][10]. - Six progressive projects are designed to enhance understanding and application of various technical aspects, ensuring a solid foundation for future research and work [14][15]. Group 6: Expected Outcomes - Upon completion of the course, participants will gain a complete embodied intelligence technology stack, enhancing their technical, engineering, and innovative capabilities [25][26]. - Participants will develop skills in building complex robot simulation environments, understanding core reinforcement learning algorithms, and applying Sim-to-Real transfer techniques [25].

Core Viewpoint - The article discusses the rapid advancements in embodied intelligence, highlighting its potential to revolutionize various industries such as manufacturing, healthcare, and space exploration through robots that can understand language, navigate complex environments, and make intelligent decisions [1]. Group 1: Embodied Intelligence Technology - Embodied intelligence aims to integrate AI systems with physical capabilities, allowing them to perceive and interact with the physical world [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in this transformative field [1]. - The core challenge in achieving true embodied intelligence lies in the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2]. Group 2: Role of MuJoCo - MuJoCo (Multi-Joint dynamics with Contact) is identified as a critical technology for embodied intelligence, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [3]. - It allows researchers to conduct millions of trials in a simulated environment, significantly speeding up the learning process while minimizing hardware damage risks [5]. - MuJoCo's advantages include advanced contact dynamics algorithms, high parallel computation capabilities, and a variety of sensor models, making it a standard tool in both academia and industry [5][7]. Group 3: Practical Applications and Learning - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations within the embodied intelligence technology stack [9]. - The course includes project-driven learning, covering topics from physical simulation principles to deep reinforcement learning and Sim-to-Real transfer techniques [9][10]. - Participants will engage in six progressively complex projects, enhancing their understanding of robot control, perception, and collaborative systems [16][21]. Group 4: Course Structure and Target Audience - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of key technical points [13][17]. - It is designed for individuals with programming or algorithm backgrounds, graduate and undergraduate students focusing on robotics or reinforcement learning, and those interested in transitioning to the field of embodied robotics [28].

"AI 的下半场是落地，而具身智能将是最佳载体"。 紧接着，Figure 又于近日宣布，其工业机器人 Figure 02 通过纯强化学习算 法，成功实现了如人类般自然流畅的行走。 强化学习驱动： 突破 Sim-to-Real 难题 责编 | 梦依丹 出品 | CSDN（ID：CSDNnews） Figure 自 2 月宣布与 OpenAI 结束合作转而拥抱完全自主研发路线后，动作频频。 先是于 2 月下旬正式发布其倾力打造的机器人操作系统 Helix ，该系统被视为 Figure 实现"真正自主"的关键基石。不仅如此，搭载该模型的 Figure 02 也已进驻物流工厂，承担起快递分拣的重任，显示了其初步的商业化潜力。 然而，仅仅在模拟环境中训练是不够的。如何将模拟环境中的学习成果成功迁移到真实的机器人身上，是一个巨大的挑战，被称为 "Sim-to-Real" 问 题。为了克服这一难题，Figure 团队采用了两种关键策略： 通过将域随机化与高频扭矩反馈控制相结合，Figure 成功地实现了零样本迁移（Zero-Shot Transfer），即无需额外的微调，在模拟环境中训练出的策 略可以直接应用于真实的 Fi ...

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].