Core Insights - The article discusses the development of DexNDM, a method aimed at solving the sim-to-real challenge in dexterous robotic manipulation, particularly in achieving stable in-hand rotation of various objects [2][5][24]. Group 1: Background and Challenges - High dexterity in remote operation of complex tools, such as using a screwdriver or hammer, has been a long-standing challenge in robotics [4]. - Traditional direct mapping remote operation methods are limited to simple tasks and cannot handle complex manipulations requiring fine motor skills [4]. - A semi-autonomous remote operation paradigm is proposed, which breaks down complex tasks into stable atomic skills that robots can execute autonomously [4]. Group 2: DexNDM Methodology - DexNDM is designed to learn general and stable atomic skills for in-hand rotation, covering a wide range of scenarios including challenging elongated and small objects [5][19]. - The method utilizes a joint-wise neural dynamics model to bridge the gap between simulation and real-world dynamics, enhancing data efficiency and generalization across different hand-object interactions [19][20]. Group 3: Achievements and Capabilities - DexNDM achieves unprecedented capabilities in continuous rotation of objects under challenging wrist postures, demonstrating superior performance compared to previous methods [9][13]. - The system allows operators to guide dexterous hands in complex tasks such as tightening screws and assembling furniture, showcasing its robustness and adaptability [7][15]. - The method's flexibility enables stable execution of tasks regardless of the wrist orientation or rotation axis required [14][15]. Group 4: Data Collection and Training - An automated data collection system, termed "Chaos Box," is developed to gather diverse real-world interaction data with minimal human intervention [21]. - A residual policy network is trained to compensate for the dynamics gap between simulation and reality, enhancing the system's performance in real-world applications [23]. Group 5: Conclusion and Future Outlook - DexNDM represents a significant advancement in addressing the sim-to-real challenge in robotics, achieving dexterous manipulation skills previously deemed impossible [24]. - The authors believe this is just the beginning, with the potential for dexterous hands to play a crucial role in the future of humanoid robotics [25].

Core Insights - The article emphasizes that the field of AI is at a pivotal moment, transitioning from early symbolic reasoning to deep learning breakthroughs and now to the rise of embodied intelligence, which is redefining human-machine relationships [1][3]. Group 1: Embodied Intelligence - Embodied intelligence is characterized by machines that can understand language commands, navigate complex environments, and make intelligent decisions in real-time, moving beyond the realm of virtual space [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are actively developing technologies in this disruptive field, indicating a competitive landscape [1][3]. - The potential impact of embodied intelligence spans across various industries, including manufacturing, healthcare, and space exploration, suggesting a transformative effect on the economy and society [1]. Group 2: Technical Challenges and Solutions - Achieving true embodied intelligence presents unprecedented technical challenges, requiring advancements in algorithms, physical simulation, robot control, and perception fusion [3]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a critical technology for embodied intelligence, serving as a high-fidelity simulation engine that connects virtual and real-world environments [4][6]. - MuJoCo allows researchers to conduct millions of trials in a simulated environment, significantly accelerating the learning process while minimizing risks associated with physical hardware [6][8]. Group 3: MuJoCo's Advantages - MuJoCo's advanced contact dynamics algorithms enable precise simulation of complex interactions between robots and their environments, making it a standard tool in both academia and industry [4][8]. - The engine supports high parallelization, allowing thousands of simulations to run simultaneously, which enhances efficiency in training AI systems [4][6]. - The technology's stability and numerical accuracy ensure reliable long-term simulations, making it a preferred choice for leading tech companies [4][6]. Group 4: Educational Initiatives - A comprehensive MuJoCo development tutorial has been created, focusing on practical applications and theoretical foundations within the context of embodied intelligence [9][11]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a thorough understanding of the technology stack [15][17]. - Participants will engage in hands-on projects that cover a range of applications, from basic robotic arm control to complex multi-agent systems, fostering both theoretical knowledge and practical skills [19][29]. Group 5: Target Audience and Outcomes - The course is designed for individuals with programming or algorithm backgrounds looking to enter the field of embodied robotics, as well as students and professionals seeking to enhance their practical capabilities [32][33]. - Upon completion, participants will possess a complete skill set in embodied intelligence, including proficiency in MuJoCo, reinforcement learning, and real-world application of simulation techniques [32][33]. - The program aims to cultivate a combination of technical, engineering, and innovative skills, preparing participants to tackle complex problems in the field [33].

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].