MotuBrain: An Advanced World Action Model for Robot Control

• URL: http://arxiv.org/abs/2604.27792v2
• Date: Fri, 01 May 2026 08:30:06 GMT
• Title: MotuBrain: An Advanced World Action Model for Robot Control
• Abstract summary: We present MotuBrain, a unified World Action Model that jointly models video and action under a UniDiffuser formulation.<n>A single model supports policy learning, world modeling, video generation, inverse dynamics, and joint video-action prediction.<n>Building on Motus, MotuBrain further introduces unified multiview modeling, an independent text stream for stronger language-action coupling.<n>Our inference stack combines step reduction, compilation, FP8 quantization, DiT caching, V2A-style action-only inference, and real-time chunked closed-loop execution.
• Score: 23.733029557644354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models generalize semantically well but often lack fine-grained modeling of world dynamics. We present MotuBrain, a unified World Action Model that jointly models video and action under a UniDiffuser formulation with a three-stream Mixture-of-Transformers architecture. A single model supports policy learning, world modeling, video generation, inverse dynamics, and joint video-action prediction, while scaling to heterogeneous multimodal data such as video-only, task-agnostic, and cross-embodiment robot data. Building on Motus, MotuBrain further introduces unified multiview modeling, an independent text stream for stronger language-action coupling, a shared cross-embodiment action representation, and an efficient post-training and deployment recipe for long-horizon real-world control. Our inference stack combines step reduction, compilation, FP8 quantization, DiT caching, V2A-style action-only inference, and real-time chunked closed-loop execution, achieving over 50x speedup over a naive baseline and up to 11 Hz inference. Experimentally, MotuBrain achieves 95.8% and 96.1% average success on RoboTwin 2.0 under clean and randomized settings, respectively, attains the strongest reported EWMScore in our WorldArena comparison, and adapts to new humanoid embodiments with only 50--100 trajectories. These results show that unified world action models can scale in generality, predictive accuracy, and real-world deployability.

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