>> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Xinqing Li等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 在具身智能中，智能体需要 感知 环境、采取 行动 ，并 预测 其行为将如何影响未来的世界状态。世界模型正是智能体的" 内部模拟器 "，它负责捕捉环境动态， 从而支持智能体对未来进行推理。通过在内部模拟和推演未来状态，智能体可以在采取实际行动前预见多种可能的结果。 World model的思想源于Model-based RL，这一概念在Ha和Schmidhuber的工作《Recurrent World Models Facilitate Policy Evolution》中得以体现。该模型利用RNN 来学习环境的一种紧凑时空表征，并在低维空间中按时序推演未来的状态。 近年来，随着生成模型的爆发，世界模型的研究呈现出前所未有的繁荣，但其模型架构和技术选型也日趋庞杂，缺乏统一的梳理。为了进行清晰的梳理。今天带 来的综述《A ...

在近20年AI发展的路线上，我们正站在⼀个前所未有的转折点。从早期的符号推理到深度学习的突破，再 到如今⼤语⾔模型的惊艳表现， AI 技术的每⼀次⻜跃都在重新定义着⼈类与机器的关系。⽽如今，具身智 能正在全面崛起。 想象⼀下这样的场景：⼀个机器⼈不仅能够理解你的语⾔指令，还能在复杂的现实环境中灵活移动，精确 操作各种物体，甚⾄在⾯对突发情况时做出智能决策。这不再是科幻电影中的幻想，⽽是正在快速成为现 实的技术⾰命。从Tesla的Optimus⼈形机器⼈到Boston Dynamics的Atlas，从OpenAI的机械⼿到Google的RT- X项⽬，全球顶尖的科技公司都在竞相布局这⼀颠覆性领域。具身智能的核⼼理念在于让AI系统不仅拥 有"⼤脑"，更要拥有能够感知和改变物理世界的"身体"。这种AI不再局限于虚拟的数字空间，⽽是能够真 正理解物理定律、掌握运动技能、适应复杂环境。它们可以在⼯⼚中进⾏精密装配，在医院⾥协助⼿术操 作，在家庭中提供贴⼼服务，在危险环境中执⾏救援任务。这种技术的潜在影响⼒是⾰命性的：它将彻底 改变制造业、服务业、医疗健康、太空探索等⼏乎所有⾏业。 然⽽，要实现真正的具身智能，还⾯临着前 ...

Core Insights - The article discusses the development of XRoboToolkit, a cross-platform framework for robot teleoperation, addressing the increasing demand for large-scale, high-quality robot demonstration datasets due to the rapid advancement of visual-language-action models (VLAs) [3]. Existing Teleoperation Solutions Limitations - Current teleoperation frameworks have various shortcomings, including limited scalability, complex setup processes, and poor data quality [4][5]. XRoboToolkit's Core Design - The framework features a three-layer architecture for cross-platform integration, comprising XR end components, robot end components, and a service layer for real-time teleoperation and stereo vision [4][5]. Data Streaming and Transmission - XRoboToolkit employs an asynchronous callback-driven architecture for real-time data transmission from XR hardware to the client, with a focus on various tracking data formats [7][9]. Robot Control Module - The inverse kinematics (IK) solver is based on quadratic programming (QP) to generate smooth movements, particularly near kinematic singularities, enhancing stability [8][10]. XR Unity Application and Stereo Vision Feedback - The framework has been validated across multiple platforms, demonstrating an average latency of 82ms, significantly lower than the 121.5ms of Open-TeleVision, with a standard deviation of 6.32ms [11][13]. - Data quality was verified through the collection of 100 data points, achieving a 100% success rate in a 30-minute continuous operation [11][14]. Application Interface and Features - The application interface includes five panels for network status, tracking configuration, remote vision, data collection, and system diagnostics, supporting various devices [16]. - Stereo vision capabilities are optimized for depth perception, with the PICO 4 Ultra outperforming in visual quality metrics [16].

Core Viewpoint - The article discusses the unprecedented advancements in AI, particularly in embodied intelligence, which is transforming the relationship between humans and machines. This technology is poised to revolutionize various industries, including manufacturing, healthcare, and space exploration [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. This technology is no longer a concept from science fiction but is rapidly becoming a reality [1]. - Major tech companies like Tesla, Boston Dynamics, OpenAI, and Google are competing in the field of embodied intelligence, focusing on creating systems that not only have a "brain" but also a "body" capable of interacting with the physical world [1][3]. Group 2: Technical Challenges - Achieving true embodied intelligence presents significant technical challenges, including the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [3][4]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a critical technology in this field, serving as a high-fidelity simulation engine that bridges the virtual and real worlds [4][6]. Group 3: Advantages of MuJoCo - MuJoCo allows researchers to create realistic virtual robots and environments, enabling millions of trials and learning experiences without risking expensive hardware. This significantly accelerates the learning process, as simulations can run hundreds of times faster than real-time [6][8]. - The technology supports high parallelism, allowing thousands of simulation instances to run simultaneously, and provides a variety of sensor models, ensuring robust and precise simulations [6][8]. Group 4: Educational Opportunities - A comprehensive MuJoCo development course has been developed, focusing on practical applications and theoretical foundations, covering topics from physical simulation principles to deep reinforcement learning [9][11]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of embodied intelligence technologies [15][17]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a smart robotic arm, implementing vision-guided grasping systems, and developing multi-robot collaboration systems, which are designed to provide hands-on experience [19][27]. - Each project is accompanied by detailed documentation and code references, facilitating a deep understanding of the underlying technologies and their applications in real-world scenarios [30][32]. Group 6: Target Audience and Outcomes - The course is suitable for individuals with programming or algorithm backgrounds looking to enter the field of embodied robotics, as well as students and professionals interested in enhancing their practical skills [32][33]. - Upon completion, participants will possess a complete skill set in embodied intelligence, including technical, engineering, and innovative capabilities, making them well-equipped for roles in this rapidly evolving industry [32][33].

Core Insights - The article emphasizes the significance of "Embodied Intelligence" in the pursuit of General Artificial Intelligence (AGI), highlighting the need for intelligent agents to perceive, reason, and act in the physical world [3][5] - The integration of physical simulators and world models is identified as a promising pathway to enhance the capabilities of robots, enabling them to transition from merely "doing" to "thinking" [3][5] Summary by Sections 1. Introduction to Embodied Intelligence - Embodied Intelligence focuses on intelligent agents that can autonomously perceive, predict, and execute actions in complex environments, which is essential for achieving AGI [5] 2. Key Technologies - Two foundational technologies, physical simulators and world models, are crucial for developing robust embodied intelligence. Physical simulators provide safe and efficient environments for training, while world models enable internal representations of the environment for predictive planning and adaptive decision-making [5] 3. Research Contributions - The article reviews recent advancements in learning embodied intelligence through the fusion of physical simulators and world models, analyzing their complementary roles in enhancing agent autonomy, adaptability, and generalization capabilities [5] 4. Robot Capability Classification - A five-level capability classification system for intelligent robots is proposed, ranging from IR-L0 (basic execution) to IR-L4 (fully autonomous), covering dimensions such as autonomy, task handling, environmental adaptability, and social cognition [8][15] 5. Core Technology Review - The article systematically reviews the latest technological advancements in legged locomotion, manipulation control, and human-robot interaction, emphasizing the importance of these capabilities in the development of intelligent robots [8] 6. Physical Simulator Comparison - A comparative analysis of mainstream simulation platforms (Webots, Gazebo, MuJoCo, Isaac Gym/Sim) is provided, focusing on their physics engine accuracy, rendering quality, and sensor component support, along with future optimization directions [13][19] 7. World Model Architecture and Applications - The article discusses representative structures of world models, including predictive networks and generative models, and their applications in embodied intelligence, particularly in autonomous driving and articulated robots [14][20]

Core Insights - The article emphasizes the significance of "Embodied Intelligence" in the pursuit of Artificial General Intelligence (AGI), highlighting the need for intelligent agents to perceive, reason, and act in the physical world [5] - The integration of physical simulators and world models is identified as a promising pathway to enhance the capabilities of robots, enabling them to transition from mere action execution to cognitive processes [5] Summary by Sections 1. Introduction to Embodied Intelligence - Embodied Intelligence focuses on intelligent agents that can autonomously perceive, predict, and execute actions in complex environments, moving towards AGI [5] - The combination of physical simulators and world models is crucial for developing robust embodied intelligence [5] 2. Key Contributions - The paper systematically reviews the advancements in learning embodied intelligence through the integration of physical simulators and world models, analyzing their complementary roles in enhancing autonomy, adaptability, and generalization of intelligent agents [5] 3. Robot Capability Classification - A five-level capability classification system (IR-L0 to IR-L4) is proposed, covering autonomy, task handling, environmental adaptability, and social cognition [9][10] - IR-L0: Basic execution with no environmental perception - IR-L1: Rule-based response in closed environments - IR-L2: Perceptual adaptation with basic path planning - IR-L3: Human-like collaboration with emotional recognition - IR-L4: Full autonomy with self-generated goals and ethical decision-making [15] 4. Review of Core Robot Technologies - The article reviews the latest technological advancements in legged locomotion, manipulation control, and human-robot interaction [11][16] 5. Comparative Analysis of Physical Simulators - A comprehensive comparison of mainstream simulators (Webots, Gazebo, MuJoCo, Isaac Gym/Sim) is provided, focusing on their physical simulation capabilities, rendering quality, and sensor support [12][18][19] 6. Advances in World Models - The paper discusses representative architectures of world models and their applications, such as trajectory prediction in autonomous driving and simulation-reality calibration for articulated robots [13][20]

Core Viewpoint - The article discusses the unprecedented advancements in AI, particularly in embodied intelligence, which is transforming the relationship between humans and machines. Major tech companies are competing in this revolutionary field, which has the potential to significantly impact various industries such as manufacturing, healthcare, and space exploration [1][2]. 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 [1]. - Leading companies like Tesla, Boston Dynamics, OpenAI, and Google are actively developing technologies in this area, emphasizing the need for AI systems to have both a "brain" and a "body" [1][2]. Group 2: Technical Challenges - Achieving true embodied intelligence presents significant technical challenges, including the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2][4]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a key technology in overcoming these challenges, serving as a high-fidelity training environment for robot learning [4][6]. Group 3: MuJoCo's Role - MuJoCo is not just a physics simulation engine; it acts as a crucial bridge between the virtual and real worlds, enabling robots to learn complex motor skills without risking expensive hardware [4][6]. - The advantages of MuJoCo include simulation speeds hundreds of times faster than real-time, the ability to conduct millions of trials in a virtual environment, and successful transfer of learned strategies to the real world through domain randomization [6][8]. Group 4: Research and Development - Numerous cutting-edge research studies and projects in robotics are based on MuJoCo, with major tech firms like Google, OpenAI, and DeepMind utilizing it for their research [8]. - Mastery of MuJoCo positions researchers and engineers at the forefront of embodied intelligence technology, providing them with opportunities to participate in this technological revolution [8]. Group 5: Practical Training - A comprehensive MuJoCo development course has been created, focusing on both theoretical knowledge and practical applications within the embodied intelligence technology stack [9][11]. - The course is structured into six weeks, each with specific learning objectives and practical projects, ensuring a solid grasp of key technical points [15][17]. Group 6: Course Projects - The course includes six progressively challenging projects, such as building a smart robotic arm, implementing vision-guided grasping systems, and developing multi-robot collaboration systems [19][27]. - Each project is designed to reinforce theoretical concepts through hands-on experience, ensuring participants understand both the "how" and the "why" behind the technologies [30][32]. Group 7: Career Development - Completing the course equips participants with a complete embodied intelligence technology stack, enhancing their technical, engineering, and innovative capabilities [31][33]. - Potential career paths include roles as robotics algorithm engineers, AI research engineers, or product managers, with competitive salaries ranging from 300,000 to 1,500,000 CNY depending on the position and company [34].

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].

Core Viewpoint - The article discusses the unprecedented advancements in AI, particularly in embodied intelligence, which is transforming the relationship between humans and machines. Major tech companies are competing in this revolutionary field, which has the potential to significantly impact various industries such as manufacturing, healthcare, and space exploration [1][2]. 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 [1]. - Leading companies like Tesla, Boston Dynamics, OpenAI, and Google are actively developing technologies in this area, emphasizing the need for AI systems to possess both a "brain" and a "body" [1][2]. Group 2: Technical Challenges - Achieving true embodied intelligence presents significant technical challenges, including the need for advanced algorithms and a deep understanding of physical simulation, robot control, and perception fusion [2][4]. - MuJoCo (Multi-Joint dynamics with Contact) is highlighted as a key technology in overcoming these challenges, serving as a high-fidelity training environment for robot learning [4][6]. Group 3: MuJoCo's Role - MuJoCo is not just a physics simulation engine; it acts as a crucial bridge between the virtual and real worlds, enabling researchers to conduct millions of trials in a simulated environment without risking expensive hardware [4][6]. - The advantages of MuJoCo include simulation speeds hundreds of times faster than real-time, the ability to test extreme scenarios safely, and effective transfer of learned strategies to real-world applications [6][8]. Group 4: Educational Opportunities - A comprehensive MuJoCo development course has been created, focusing on practical applications and theoretical foundations, covering topics from physics simulation to deep reinforcement learning [9][10]. - The course is structured into six modules, each with specific learning objectives and practical projects, ensuring a solid grasp of embodied intelligence technologies [11][13]. Group 5: Project-Based Learning - The course includes six progressively challenging projects, such as building a robotic arm control system and implementing vision-guided grasping, which are designed to reinforce theoretical concepts through hands-on experience [15][17][19]. - Each project is tailored to address specific technical points while aligning with overall learning goals, providing a comprehensive understanding of embodied intelligence [12][28]. Group 6: Career Development - Completing the course equips participants with a complete skill set in embodied intelligence, enhancing their technical, engineering, and innovative capabilities, which are crucial for career advancement in this field [29][31]. - Potential career paths include roles as robot algorithm engineers, AI research engineers, or product managers, with competitive salaries ranging from 300,000 to 1,500,000 CNY depending on the position and company [33].