清华最新RoboScape：基于物理信息的具身世界模型~

Core Viewpoint - The article discusses the development of RoboScape, a physics-informed embodied world model that enhances video generation quality by integrating physical knowledge into the modeling process, addressing limitations in existing models related to physical perception and object manipulation [2][22]. Research Background and Core Issues - The existing models in embodied intelligence face significant limitations in physical perception, particularly in robot scenarios involving contact, leading to unrealistic object deformation and motion discontinuities [2]. - Current attempts to integrate physical knowledge are categorized into three types: physical prior regularization, knowledge distillation from physical simulators, and material field modeling, each with its own limitations [2]. Core Method - 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]. Data Processing Pipeline - A four-step processing pipeline is designed to construct a multimodal embodied dataset with physical priors, based on the AGIBOT-World dataset [5]. RoboScape Model Architecture - The architecture utilizes a self-regressive Transformer framework to generate controllable robot videos, integrating physical knowledge through two auxiliary tasks: physical attribute labeling and video slicing [7]. Time Depth Prediction - To enhance 3D geometric consistency, a time depth prediction branch is added to the RGB prediction backbone, employing a dual-branch cooperative self-regressive Transformer [9]. 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 [10]. Joint Training Objectives - The overall training objective integrates various loss functions to balance the contributions of different components [12]. Experimental Validation - The model's performance is evaluated across three dimensions: appearance fidelity, geometric consistency, and action controllability, showing superior results compared to baseline models [14][20]. Dataset and Implementation Details - The dataset comprises 50,000 video segments covering 147 tasks and 72 skills, with training conducted on 32 NVIDIA A800 GPUs over five epochs [15]. Downstream Application Validation - In robot policy training, the model demonstrates performance close to real data training results, indicating the effectiveness of synthetic data for complex tasks [18]. 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 in practical scenarios [22][23].