Group 1 - The article emphasizes the value of embodied world models, robotic control, and data collection as significant industry directions with certain barriers to entry [2] - The company seeks to collaborate with experts in the field to develop courses or practical projects related to these topics, aiming to provide insights for professionals currently working in these areas [2][3] - Interested individuals with at least one year of industry experience or a publication in a CCF-A level conference are encouraged to participate in the collaboration [3] Group 2 - The company offers competitive salaries and resource sharing for collaborators, with opportunities for part-time involvement [5]

Group 1 - The article emphasizes the importance of embodied world models, robotic control, and data collection as valuable directions in the industry, despite existing barriers to entry [2] - The company seeks to collaborate with experts in the field to develop courses or practical projects related to these topics, aiming to provide insights for professionals currently working in these areas [2] - Interested parties are encouraged to contact the company for further consultation regarding course design and presentation materials related to embodied world models, control, and data collection [3] Group 2 - The company is looking for individuals engaged in embodied research who have either published a paper in a CCF A-level conference or possess over one year of industry experience [4] - The company offers competitive salaries and resource sharing, with opportunities for part-time involvement for interested candidates [6] - Specific requirements for collaboration are outlined, indicating a focus on expertise and experience in the relevant fields [7]

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]

Core Insights - The rapid evolution of robotic movement capabilities is highlighted, but understanding complex tasks remains challenging for robots [1] - The introduction of the "WoW" embodied world model by a Chinese research team represents a significant advancement in robotic intelligence [1] Group 1: Technological Advancements - The "WoW" embodied world model allows robots to simulate human-like thinking and decision-making, generating future prediction videos that align with physical laws [5] - The model enables robots to connect imagined movements with real-world execution, enhancing their interaction with the environment [5] Group 2: Data Collection and Training - The research team has collected millions of real interaction data points to ensure the world model can operate effectively in diverse real-world scenarios [8] - The model is designed to adapt to various types of robots, including humanoid and robotic arms, and can be applied across multiple settings such as homes, supermarkets, and logistics [10] Group 3: Open Access and Applications - The "WoW" model is open to global researchers and developers, facilitating broader applications and innovations in robotics [10] - It can accurately simulate extreme scenarios, such as water spilling on a computer, providing crucial data for training that is difficult to obtain through real-world testing [10]

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].