10000 Hours of Human Data, Trains the World's First Whole-Body Mobile Manipulation Implicit World-Action Model
"Being-M0.7" is the world's first latent world-action model for whole-body mobile manipulation in humanoid robots, developed by Zhi Zai Wu Jie (Beyond Being). Trained on over 10,000 hours of human-centric multimodal data, it aims to overcome key industry challenges: the high cost and scarcity of real robot demonstration data, the computational inefficiency of pixel-level video prediction models, and the lack of full-body coordination in existing approaches.
The model is based on a Vision-Motion Mixture-of-Transformers (MoT) architecture, which allows training on a mixture of paired video-motion data, pure video data, and pure motion sequences. A key design is a unified motion representation that bridges human and robot morphology, enabling knowledge transfer from vast human behavioral data to specific robot control.
After pre-training on human data, the model is adapted to a real robot (Unitree G1) using a small amount of teleoperated demonstration data via a lightweight "Action Expert" module. This process decouples low-frequency world planning from high-frequency motion control.
The model was tested in four challenging real-world demos: fishing a toy fish from water (liquid interaction), retrieving an object using a mirror (visual reasoning), a multi-step pick-and-place task, and obstacle avoidance while carrying a box. In comparative tests against other models, Being-M0.7 showed stronger performance in tasks requiring indirect reasoning and full-body coordination.
This work represents a shift in humanoid robotics competition from hardware spectacle to model capabilities rooted in scalable data and training paradigms, using human experience as a foundation for physical world understanding and action.
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