最近收到越来越多合作伙伴和中小公司的诉求，期望具身智能之心团队能够在方案和数采、技术升级、 企业培训等多个方向上赋能。 岗位说明 主要面向具身课程开发、方案研发、硬件研发、培训合作（B端主要面向企业和高校、研究院所培训，C 端面向较多学生、求职类人群）。 联系我们 感兴趣的可以添加微信oooops-life做进一步咨询。 虽然从上半年开始，我们一直在筹办相关事宜。但众人拾柴火焰高，要推动大的行业进步，需要更多优 秀的伙伴加入我们。 现面向全球的具身领域从业者发出邀请函，具身智能之心期望能够和您在技术服务、培训、课程开发与 科研辅导等多个领域展开合作。 我们将提供高额的酬金与丰富的行业资源。 主要方向 包括但不限于：VLA、VLN、Diffusion Policy、强化学习、VLA+RL、遥操作、动捕、sim2real、多模态 大模型、仿真、运动控制、端到端、3D感知等多个方向。 ...

Core Viewpoint - The article discusses the evolution and current trends in embodied intelligence technology, emphasizing the integration of various models and techniques to enhance robotic capabilities in real-world environments [3][10]. Group 1: Technology Development Stages - The development of embodied intelligence has progressed through several stages, starting from grasp pose detection to behavior cloning, and now to diffusion policy and VLA models [7][10]. - The first stage focused on static object grasping with limited decision-making capabilities [7]. - The second stage introduced behavior cloning, allowing robots to learn from expert demonstrations but faced challenges in generalization and error accumulation [7]. - The third stage, marked by the introduction of diffusion policy methods, improved stability and generalization by modeling action sequences [8]. - The fourth stage, beginning in 2025, explores the integration of VLA models with reinforcement learning and world models to enhance predictive capabilities and multi-modal perception [9][10]. Group 2: Key Technologies and Techniques - Key technologies in embodied intelligence include VLA, diffusion policy, and reinforcement learning, which collectively enhance robots' task execution and adaptability [5][10]. - VLA models combine visual perception, language understanding, and action generation, enabling robots to interpret human commands and perform complex tasks [8]. - The integration of tactile sensing with VLA models expands the sensory capabilities of robots, allowing for more precise operations in unstructured environments [10]. Group 3: Industry Implications and Opportunities - The advancements in embodied intelligence are leading to increased demand for engineering and system capabilities, transitioning from theoretical research to practical deployment [10][14]. - There is a growing interest in training and deploying various models, including diffusion policy and VLA, on platforms like Mujoco and IsaacGym [14]. - The industry is witnessing a surge in job opportunities and research interest, prompting many professionals to shift focus towards embodied intelligence [10].

Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the group entry process, it is advised to include a note with the institution/school, name, and research direction [3]

Group 1 - The article announces the recruitment of teachers for embodied intelligence training, targeting both B-end (business) and C-end (consumer) training services, with compensation above industry standards [1] - The training covers various advanced topics including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, sim2real, multimodal large models, simulation, motion control, and target navigation [2] - B-end training is aimed at enterprises, universities, and research institutions, while C-end training focuses on students and job seekers, with responsibilities including curriculum design and material preparation [3] Group 2 - Candidates are required to have a doctoral degree or higher (including those currently enrolled), with a preference for those who have published two papers in A-level or Q1 journals/conferences, or have two years of industry experience [3] - Interested individuals can add a specified WeChat contact for further inquiries [4]

Core Viewpoint - The article discusses recent advancements in reinforcement learning (RL) and its applications in robotics, particularly focusing on the VLA (Vision-Language Action) models and diffusion policies, highlighting their potential to handle complex tasks that traditional RL struggles with [2][4][35]. Method Paradigms - Traditional RL and imitation learning combined with Sim2Real techniques are foundational approaches in robotics [3]. - VLA models differ fundamentally from traditional RL by using training data distributions to describe task processes and goals, allowing for the execution of more complex tasks [4][35]. - Diffusion Policy is a novel approach that utilizes diffusion models to generate continuous action sequences, demonstrating superior capabilities in complex task execution compared to traditional RL methods [4][5]. Application Scenarios - The article categorizes applications into two main types: basic motion control for humanoid and quadruped robots, and complex/long-range operational tasks [22][23]. - Basic motion control primarily relies on RL and Sim2Real, with current implementations still facing challenges in achieving fluid motion akin to human or animal movements [22]. - For complex tasks, architectures typically involve a pre-trained Vision Transformer (ViT) encoder and a large language model (LLM), utilizing diffusion or flow matching for action output [23][25]. Challenges and Future Directions - The article identifies key challenges in the field, including the need for better simulation environments, effective domain randomization, and the integration of external goal conditions [35]. - It emphasizes the importance of human intention in task definition and the limitations of current models in learning complex tasks without extensive human demonstration data [35][40]. - Future advancements may involve multi-modal input predictions for task goals and the potential integration of brain-machine interfaces to enhance human-robot interaction [35].

Group 1 - The establishment of a technical exchange group focused on embodied intelligence technologies, including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is recommended to include the organization/school, name, and research direction in the remarks [3]

Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is recommended to include a note with the institution/school, name, and research direction [3]

Core Insights - The article provides a comprehensive overview of various surveys and research papers related to embodied intelligence, focusing on areas such as vision-language-action models, reinforcement learning, and robotics applications [1][2][3][4][5][6][7][8][9] Group 1: Vision-Language-Action Models - A survey on Vision-Language-Action (VLA) models highlights their significance in autonomous driving and human motor learning, discussing progress, challenges, and future trends [2][3][8] - The exploration of VLA models emphasizes their applications in embodied AI, showcasing various datasets and methodologies [8][9] Group 2: Robotics and Reinforcement Learning - Research on foundation models in robotics addresses applications, challenges, and future directions, indicating a growing interest in integrating AI with robotic systems [3][4] - Deep reinforcement learning is identified as a key area with real-world successes, suggesting its potential for enhancing robotic capabilities [3] Group 3: Multimodal and Generative Approaches - The article discusses multimodal fusion and vision-language models, which are crucial for improving robot vision and interaction with the environment [6] - Generative artificial intelligence in robotic manipulation is highlighted as an emerging field, indicating a shift towards more sophisticated AI-driven robotic systems [6] Group 4: Datasets and Community Engagement - The article encourages engagement with a community focused on embodied intelligence, offering access to a wealth of resources, including datasets and collaborative projects [9]

Core Insights - The article provides a comprehensive overview of various surveys and research papers related to embodied intelligence, focusing on areas such as vision-language-action models, reinforcement learning, and robotics applications [1][2][3][4][5][6][8][9] Group 1: Vision-Language-Action Models - A survey on Vision-Language-Action (VLA) models highlights their significance in autonomous driving and human motor learning, discussing progress, challenges, and future trends [2][3][8] - The exploration of VLA models emphasizes their applications in embodied AI, showcasing a variety of datasets and methodologies [5][8][9] Group 2: Robotics and Reinforcement Learning - Research on foundation models in robotics addresses applications, challenges, and future directions, indicating a growing interest in integrating AI with robotic systems [3][4] - Deep reinforcement learning is identified as a key area with real-world successes, suggesting its potential for enhancing robotic capabilities [3][4] Group 3: Multimodal and Generative Approaches - The article discusses multimodal fusion and vision-language models, which are crucial for improving robot vision and interaction with the environment [6][8] - Generative artificial intelligence in robotic manipulation is highlighted as an emerging field, indicating a shift towards more sophisticated AI-driven solutions [6][8] Group 4: Datasets and Community Engagement - The article encourages engagement with a community focused on embodied intelligence, offering access to a wealth of resources, including datasets and collaborative projects [9]