{"work":{"id":"b20ea91b-d63e-47db-8e27-e2dc8e9aa95b","openalex_id":null,"doi":null,"arxiv_id":"2604.27792","raw_key":null,"title":"MotuBrain: An Advanced World Action Model for Robot Control","authors":null,"authors_text":null,"year":2026,"venue":"cs.RO","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.","external_url":"https://arxiv.org/abs/2604.27792","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-03T21:38:58.706514+00:00","pith_arxiv_id":"2604.27792","created_at":"2026-05-13T05:07:18.280416+00:00","updated_at":"2026-07-03T21:38:58.706514+00:00","title_quality_ok":true,"display_title":"MotuBrain: An Advanced World Action Model for Robot Control","render_title":"MotuBrain: An Advanced World Action Model for Robot Control"},"hub":{"state":{"tier_text":"hub","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":11,"external_cited_by_count":null},"tier":"hub","role_counts":[{"context_role":"background","n":1}],"polarity_counts":[{"context_polarity":"background","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}