Motus2: A Self-Evolving General World Model for Dexterous Manipulation
General embodied agents should perceive, predict, act, evaluate, and improve within a unified system. World models have shown great promise in building such agents, yet existing models typically append an action output head to a world simulator, without coupling them into a closed decision-and-learning loop for policy improvement. We present Motus2, a self-evolving general world model for dexterous manipulation. Motus2 advances world modeling through model scaling and data scaling. For model scaling, a single model with shared weights exposes three control interfaces: a policy (world-action model), a simulator (action-conditioned world model), and an evaluator (value model). The policy pr...
| Authors | Hongzhe Bi, Zihao Zhou, Yihang Tang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 31 August 2026 |
| Last updated at source | 10 September 2026 |
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