跨维智能开源基于生成式仿真世界模型的具身智能工具链EmbodiChain

Core Insights - The core idea of EmbodiChain is to replace data collection with generation, creating an online data stream that eliminates the inefficiencies of traditional data generation and storage methods [2] Group 1: EmbodiChain Overview - EmbodiChain is the world's first embodied intelligence toolchain based on generative simulation world models, capable of automatically training VLA models and deploying them on real robots without relying on real data [1] - The toolchain utilizes 100% synthetic data for training, enabling zero-shot transfer from simulation to reality [1] - It features an end-to-end automated process that integrates generative scene construction and agent skill exploration, significantly reducing the training time from months to days [1] Group 2: Technological Innovations - The technology framework of EmbodiChain includes three innovative modules: world generation, data augmentation and self-repair, and privileged information driving [2] - The world generation module can automatically create physically consistent 3D scenes and task environments from minimal real samples or language instructions [2] - The data augmentation module enhances model robustness by randomizing physical parameters and generating corrective trajectories during task failures, creating a closed-loop learning mechanism [2] Group 3: Validation and Future Plans - Cross Dimension Intelligence conducted extreme tests using 100% synthetic data to train the Sim2Real-VLA model, which outperformed traditional methods that rely on real data in terms of operational success rates in real environments [3] - The company plans to release the VLA base models and specific task examples trained by EmbodiChain to provide standardized infrastructure for the community [3] - The open-sourcing of EmbodiChain is a crucial step for the company in promoting collaborative development within the industry, aiming to make it a fundamental resource in the field of embodied intelligence [3]