具身世界模型、运控、数采相关课程设计、制作。 招募具身世界模型&数采相关的合作伙伴！ 最近后台收到很多同学关于具身世界模型、机器人运控、数采相关的内容咨询，确实是行业比较有价值的 方向，但又存在一定的门槛。 具身智能之心期望和领域大牛一起研发相关方向的课程或实战项目，为正在从事相关工作的同学提供更多 见解。 如果有大佬感兴趣，可以添加峰哥微信：oooops-life做进一步咨询。 合作内容 一些要求 正在从事具身领域研究的童鞋，我们期望您至少发表一篇ccf-a级别会议或有1年以上的工业界经验。 待遇说明 高于行业水平的薪资和资源共享，可兼职，感兴趣的可以添加负责人微信做进一步沟通。 ...

最近后台收到很多同学关于具身世界模型、机器人运控、数采相关的内容咨询，确实是行业比较有价值的 方向，但又存在一定的门槛。 具身智能之心期望和领域大牛一起研发相关方向的课程或实战项目，为正在从事相关工作的同学提供更多 见解。 如果有大佬感兴趣，可以添加峰哥微信：oooops-life做进一步咨询。 合作内容 具身世界模型、运控、数采相关课程设计、PPT制作。 招募世界模型&人形运控&数采相关的合作伙伴！ 待遇说明 高于行业水平的薪资和资源共享，可兼职，感兴趣的可以添加负责人微信做进一步沟通。 一些要求 正在从事具身领域研究的童鞋，我们期望您至少发表一篇ccf-a级别会议或有1年以上的工业界经验。 ...

Group 1 - The article discusses the rising interest in embodied world models, highlighting their industrial and research value [1] - The company is recruiting two lecturers to help develop courses or tutoring content related to world models [2] - There is an emphasis on collaboration with individuals who have a strong background in the field, specifically those with a PhD or higher who have published at least one paper in a CCF-A level conference [5] Group 2 - The compensation offered for the positions is above industry standards, and the roles can be part-time [6] - Interested individuals are encouraged to contact the responsible person via WeChat for further communication [6]

央视网消息：当前，机器人的运动能力正在迅速进化。但是，要让它们像人一样理解一些事情还是比较困难的。日前，我国科研团 队开源出一个名叫"WoW"的具身世界模型，它有什么进步？ 这里是北京人形机器人创新中心，各种形态的机器人本体正在进行具身智能数据采集和动作模型训练。这台"天工"机器人正在进行 的就是自主地1:1复刻这个视频中的动作姿态，而这个视频就是机器人在行动之前"想象出来"的预演画面，可以用来指导它与真实世 界的交互。这样从想象预演到动作执行的"知行合一"的能力，依托的就是由科研团队自主研发的具身世界模型。 WoW具身世界模型项目算法负责人贾沛东介绍，他们采集了百万级别真实交互的具身智能数据，让世界模型能够在真实非常泛化的 场景下真正去操作。 具身世界模型向全球研究者与开发者开放。可以适配人形、类人形、机械臂等不同本体机器人，覆盖家居、商超、工业、物流等多 种场景。还能高精度模拟水洒在电脑上等极端情况，为真机训练难以实现的数据采集提供重要补充。 WoW具身世界模型项目负责人池晓威介绍，世界模型本质上就是AI模拟人类思考和决策的时候，去进行想象和预测的一个模型。它 需要去生成符合物理规律的未来预测视频，帮助机 ...

Core Insights - The article discusses the limitations of existing embodied intelligence models in physical perception, particularly in robot scenarios involving contact, highlighting the need for better integration of physical knowledge into these models [3][20]. Research Background and Core Issues - Current models rely heavily on visual token fitting and lack physical knowledge awareness, leading to unrealistic object deformation and motion discontinuities in generated videos [3]. - Previous attempts to integrate physical knowledge have been limited to narrow domains or complex pipelines, indicating a need for a unified and efficient framework [3]. Core Methodology - The focus is on learning an embodied world model as a dynamic function to predict the next visual observation based on past observations and robot actions [4]. - A four-step processing pipeline is designed to create a multimodal dataset with physical priors, utilizing the AGIBOT-World dataset [5]. Data Processing Pipeline - The pipeline includes physical attribute annotation, video slicing, segment filtering, and segment classification to ensure effective training data [5][8]. Time Depth Prediction - A dual-branch cooperative autoregressive Transformer (DCT) is introduced to enhance 3D geometric consistency, ensuring causal generation through temporal and spatial attention layers [7]. Adaptive Keypoint Dynamic Learning - The model employs self-supervised tracking of contact-driven keypoints to implicitly encode material properties, enhancing the modeling of object deformation and motion patterns [8]. Joint Training Objectives - The overall training objective integrates various loss functions to balance the contributions of different components in the model [10]. Experimental Validation - The model's performance is evaluated across appearance fidelity, geometric consistency, and action controllability, demonstrating superior results compared to baseline models [12][18]. Dataset and Implementation Details - The study utilizes the AgiBotWorldBeta dataset, comprising 50,000 video segments across 147 tasks, and employs advanced models for comparison [13]. Downstream Application Validation - The model shows effectiveness in training robot policies, achieving performance close to real data training results, indicating the utility of generated data for complex tasks [16]. Conclusion and Future Plans - RoboScape effectively integrates physical knowledge into video generation without relying on external physics engines, with plans to combine generative world models with real robots for further validation [20][21].