Solis: Streaming Unified World Models for Robotic Interaction

Robotic interaction couples actions, changes in the environment, and progress toward a task. Modeling these modalities jointly offers a foundation for both generating actions toward desired outcomes and anticipating the consequences of actions. We present Solis, a streaming unified world model that learns a joint distribution over observations, actions, and progress-derived values using a single video diffusion transformer. Using different inputs as conditions, the same model can be used for simulation, planning, or evaluation. For practical deployment, closed-loop feedback is important for adapting to changes in the environment, yet restarting diffusion sampling after every observation makes frequent feedback costly. To address this, we employ temporal block-causal attention and natively pretrain our model with independently sampled chunk noise levels. At inference, we adopt a rolling diffusion schedule to maintain future video, action, and value chunks at progressively increasing noise levels: the leading chunk becomes executable while later chunks remain partially denoised. After execution, the horizon shifts with a new observation and a fresh noise chunk enters. This design raises feedback frequency without extra computational cost. With cross-embodiment pretraining and per-benchmark post-training, Solis achieves 98.4% average closed-loop success across four LIBERO suites and 88.8% and 89.1% across 12 RoboTwin 2.0 tasks in clean and randomized scenes. Furthermore, Solis shows strong future prediction and accurate value estimation for task evaluation.