跨维智能开源具身智能工具链 构建永不停歇的“在线数据流”

Core Insights - The article discusses the launch of EmbodiChain, an open-source toolchain for embodied intelligence that utilizes generative simulation world models, aimed at accelerating the practical application of embodied intelligence [1] Group 1: EmbodiChain Overview - EmbodiChain is the world's first toolchain capable of automatically training VLA models and deploying them on real robots without relying on real data, using 100% synthetic data for training [1] - The toolchain creates an efficient closed-loop process of "simulation-training-deployment" by integrating generative scene construction and agent skill exploration [1] - It features automated generation of task scenarios and training data, reducing the time required for high-quality training processes from months to days [1] Group 2: Technological Innovations - The core philosophy of EmbodiChain is "generating instead of collecting," establishing a continuous "online data stream" that eliminates the traditional inefficient "generate-store-read" model [2] - The technology framework includes three innovative modules: - World Generation, which automatically creates physically consistent 3D scenes and task environments from minimal real samples or language instructions [2] - Data Augmentation and Self-Repair, which enhances model robustness by randomizing physical parameters and generating corrective trajectories during task failures [2] - Privileged Information Driving, providing unseen "God's eye view" information to help models understand physical properties rather than just surface pixels [2] Group 3: Validation and Future Plans - Cross Dimension Intelligence conducted extreme tests using 100% synthetic data to train the Sim2Real-VLA model, achieving significantly higher operational success rates in real environments compared to methods relying on real data [3] - The results indicate that generative simulation data is not only feasible but may outperform traditional methods by avoiding overfitting noise [3] - The company plans to gradually release the VLA base models and various task examples trained by EmbodiChain, providing a standardized infrastructure for the community [3]