Moushen Brain Raises Nearly 100 Million Yuan in Pre-A Round: Fudan Professor and Ex-Intel Chief Scientist Create 'Brain' for Robots

Chinese startup Motion Brain (眸深智能) has raised nearly 100 million yuan (about $13.8 million) in a Pre-A funding round. Investors include industry investment platform Jinyue Investment, co-founded by a major Chinese asset management company, a Hong Kong consortium, and several public companies, as well as Chuanghehui Capital and previous investor Xuhui Capital. This is the second round in two months after a Pre-A round of 300 million yuan in May 2026. Additionally, a Pre-A+ round of 500 million yuan is in the process of closing. Since the beginning of the year, the company's valuation has increased more than tenfold. Motion Brain was founded in January 2025 by Professor Chen Tao, director of Fudan University's Deep Learning Laboratory; former Intel chief scientist in China Zhang Yimin; and serial entrepreneur Mu Zelin. Key employees have experience at Huawei HiSilicon, Intel, and Nvidia. The company has been building its technological foundation since 2022. It developed MLD (Latent Diffusion Model), which for the first time mapped motions into latent space and applied a diffusion model to generate natural movements. In September 2023, MotionGPT was introduced—a model that breaks down human poses into approximately 3,000 "motion tokens" (similar to tokens in LLMs), enabling robots to perform new actions without specific training (Zero-Shot). By 2026, seven generations of models had been released. Based on these, STI-WM (Spatio-Temporal Unified World Action Model) was created—a framework for long-term planning, closed-loop control, and physical interaction. It uses a ratio of 80% internet video, 10% motion capture data, and 10% real robot data, reducing the need for real data by 90% and improving accuracy to 99%. The T²MB (Task*Task Motion Brain) model, announced in March, allows robots to learn autonomously on site without sending data to the cloud, improving task execution accuracy by 25%. The company's work has been cited in Nvidia's latest models (ARDY). Technical advantages include compressing models from hundreds of billions to tens of billions of parameters, reducing on-device latency from 200 ms to 10 ms, cutting computing costs from 200,000 yuan to 10,000 yuan per model, and adapting to Chinese chips (HiSilicon Ascend, Horizon Robotics, Enflame). The compression work received the IJCAI 2025 Best Paper Award—the only one for a mainland Chinese team in the past five years.