2026年1月20日，跨维智能宣布正式开源基于生成式仿真世界模型的具身智能工具链EmbodiChain。 EmbodiChain是全球首个能够自动训练VLA模型并成功真机部署的基于生成式仿真世界模型的具身智能 工具链。它无需依赖真实数据，通过100%合成数据训练VLA模型，且能直接部署于真实机器人，实现 零样本虚实迁移。 在大语言模型领域，海量互联网文本数据催生了智能的涌现。但这一成功范式在机器人领域却难以复 制。核心矛盾在于数据的本质差异：LLM依赖的是存量数据的清洗，而具身智能需要的是符合物理规 律的增量数据。物理时间的流逝与人力成本的边界，始终限制着数据规模的突破。 EmbodiChain的开源是跨维智能推动行业协同发展的关键一步。其目标是将EmbodiChain打造为具身智 能领域的"水电煤"，让研究者摆脱数据采集的体力劳动与存储压力，推动具身智能研究和应用的加速落 地。 而EmbodiChain的核心理念是"以生成替代采集"。它通过生成式仿真技术，构建了一条永不停歇的"在线 数据流"，彻底摒弃了传统"生成-存储-读取"的低效模式。其技术框架包含三大创新模块： 不同于Sora等"视频生成式世界模型"， ...

世界生成：通过Real2Sim与Gen2Sim模块，引擎能够基于少量真实样本或语言指令，自动生成物理一致 的3D场景与任务环境，实现数据生产的完全自动化。 1月20日，跨维智能宣布正式开源基于生成式仿真世界模型的具身智能工具链EmbodiChain，为具身智 能模型的实用化提供了完整开源基准，推动具身智能研究和应用的加速落地。 据介绍，EmbodiChain是全球首个能够自动训练VLA模型并成功真机部署的基于生成式仿真世界模型的 具身智能工具链。它无需依赖真实数据，通过100%合成数据训练VLA模型，且能直接部署于真实机器 人，实现零样本虚实迁移。 基于端到端自动化流程，EmbodiChain融合生成式场景构建与智能体技能探索，打造"仿真—训练—部 署"的高效闭环；首创任务场景与训练数据的自动化生成技术，让高质量训练流程的构建从数月缩短至 数天；EmbodiChain还构建了覆盖自动场景生成、技能发现到真机验证的全链路评估体系，为具身智能 模型的实用化提供了完整开源基准。 在大语言模型领域，海量互联网文本数据催生了智能的涌现。但这一成功范式在机器人领域却难以复 制。核心矛盾在于数据的本质差异：LLM依赖的是存 ...

Core Viewpoint - The article discusses the emergence of a new paradigm in embodied intelligence, marked by the open-sourcing of EmbodiChain, which enables robots to be trained entirely on synthetic data and deployed in the real world without any real-world samples, signaling a shift towards data democratization in the industry [2][3][4]. Group 1: EmbodiChain and Its Impact - EmbodiChain is the world's first toolchain for embodied intelligence that can train robots using synthetic data and deploy them in real-world scenarios without any real samples, indicating the arrival of a data-equalization era [3][4]. - The open-sourcing of EmbodiChain is seen as a potential game-changer for the industry, allowing researchers and startups to generate their own training data and models, thus breaking the data monopoly held by a few large companies [14][26]. - The system operates through a closed-loop process of "dreaming - learning - validating," which eliminates the need for original physical machines [5][20]. Group 2: Technical Innovations - The first phase of the Real2Sim process includes two data generation paths: DexGen, which generates simulation scenes based on natural language, and DexDyna, which converts real operation videos into simulative action sequences [6][7]. - The second phase, Sim Data Scaling, allows for the intelligent expansion of data based on a few "seed" scenarios, achieving millions of data points through generative simulation technology [9]. - The final phase, Sim2Real, enables models trained entirely on synthetic data to be deployed directly on real robots, achieving zero-shot transfer and breaking the industry norm of mixing synthetic and real data [9][10]. Group 3: Efficiency Law and Market Potential - The article introduces the Efficiency Law, which states that the key variable determining the performance ceiling of embodied models is the rate of high-quality data generation, contrasting with the traditional Scaling Law observed in large language models [17][18]. - EmbodiChain serves as the first high data generation rate engine, transitioning the industry from a data-driven to an engine-driven paradigm, akin to the shift from manual to automated production [20][21]. - The company has already begun mass production of humanoid robots, with over 100 units shipped and nearly 100 million yuan in revenue, showcasing its commercial viability [24]. Group 4: Future Vision and Ecosystem Development - The ultimate vision for EmbodiChain is to create a complete evolutionary environment for robots, where not only strategies but also robot forms and perception systems can evolve within a physical engine [21][22]. - The open-sourcing of EmbodiChain is viewed as the beginning of an ecosystem-building effort, emphasizing the belief that the next breakthrough in embodied intelligence will arise from a standardized, shared infrastructure rather than closed proprietary models [26].

https://www.techrxiv.org/doi/full/10.36227/techrxiv.176153394.41323502 开源主页: 机器之心发布 论文地址: https://dexforce.com/embodichain/index.html#/ 代码仓库: https://github.com/DexForce/EmbodiChain 技术文档: https://dexforce.github.io/EmbodiChain/introduction.html 大语言模型的爆发，让大家见证了 Scaling Law 的威力：只要数据够多、算力够猛，智能似乎就会自动涌现。但在机器人领域，这个公式似乎失效了。 不同于互联网上唾手可得的万亿级文本，机器人所需的、经过 3D 标定且符合物理规律的高质量交互数据，极度稀缺且昂贵。正因如此，数据采集范式成为了近 年来行业研究的绝对焦点。 可以看到，整个行业正在向着更低成本、更便捷的方向全速推进： 从昂贵的遥操设备，到基于动捕手套的灵巧手捕捉和更加便携式的夹爪方案，再到如今甚至不 再需要佩戴手套、仅凭双手演示即可采集数据的创新方案。 这些轻量化的数采 ...

Core Viewpoint - The launch of DexForce W1 Pro by Kuawei Intelligence represents a significant advancement in humanoid robotics, combining hardware performance and software efficiency to expand the boundaries of embodied intelligence [2][10]. Group 1: Product Features - DexForce W1 Pro integrates a modular multi-layer development interface from X-Wiz and leverages the EmbodiChain platform for enhanced data augmentation and generation capabilities, enabling high-precision and stable robotic tasks in real-world scenarios [3]. - Key technical features include high-precision pure visual 3D perception with a self-developed dual-camera sensor and a general perception model, sub-millimeter precision operations with harmonic joints and omnidirectional motion chassis, and a new paradigm of embodied intelligence through Engine-driven Sim2Real VLA [3][5]. Group 2: Application Scenarios - The advanced capabilities of DexForce W1 Pro are applicable in various scenarios, including: - Research and education, providing a high-precision platform for VLA model training and robotic skill learning [5]. - Home assistance, executing tasks like organizing, delivering items, and meal preparation [5]. - Commercial services, enhancing customer experience in exhibitions, hotels, and retail environments [5]. - Intelligent manufacturing, efficiently handling complex tasks such as flexible assembly and material sorting on production lines [5]. Group 3: Market Position and Impact - Kuawei Intelligence has achieved widespread commercial deployment across over 50 industries, including automotive parts, new energy, 3C electronics, aerospace, logistics, home appliances, chemicals, healthcare, and education, serving notable clients in these sectors [7]. - The introduction of the open-source EmbodiChain platform addresses the challenge of data scarcity in training embodied intelligence models, transforming expensive and inefficient data collection into a low-cost, scalable, and extensible data pipeline for robotic skills [9][10]. Group 4: Future Directions - Kuawei Intelligence aims to focus on three main areas for continuous iteration: enhancing the perception robustness of W1 in various environments, improving the efficiency of online data generation and training models on the EmbodiChain platform, and expanding the skills and application scenarios of W1 [10].