ROSE: Reconstructing Objects, Scenes, and Trajectories from Casual Videos for Robotic Manipulation

In this paper, we build a real-to-sim-to-real (Real2Sim2Real) system for robot manipulation policy learning from casual human videos. We propose a new framework, ROSE, that directly leverages casual videos to reconstruct simulator-ready assets, including objects, scenes, and object trajectories, for training manipulation policies with reinforcement learning in the simulation. Unlike existing real-to-sim pipelines that rely on specialized equipment or time-consuming and labor-intensive human annotation, our pipeline is equipment-agnostic and fully automated, facilitating data collection scalability. From casual monocular videos, ROSE enables the direct reconstruction of metric-scale scenes, objects, and object trajectories in the same gravity-calibrated coordinate for robotic data collection in the simulator. With ROSE, we curate a dataset of simulator-ready scenes from casual videos from our own capture and the Internet, and create a benchmark for real-to-sim evaluation. Across a diverse suite of manipulation tasks, ROSE outperforms the existing baselines, laying the groundwork for scalable robotic data collection and achieving efficient Real2Sim2Real deployment.