robotic-control

This paper introduces ODEWorld, a continuous-time latent world model using Physical-Time Flow (PT-Flow) that learns a latent velocity field parameterized by an ordinary differential equation, enabling arbitrary temporal resolution, backward prediction, and improved planning for video generation and robotic control.

This paper introduces In-Context World Modeling (ICWM), a framework that enables robot policies to infer system variables from self-generated interactions, allowing adaptation to novel configurations without parameter updates by treating system identification as an in-context adaptation problem. It outperforms standard VLA baselines on novel camera viewpoints in simulation and real-world experiments.

Introduces WarmPrior, a method that replaces the standard Gaussian source in flow-matching policies with a temporally grounded prior from recent action history, consistently improving success rates on robotic manipulation tasks by producing straighter probability paths.