Any-point Trajectory Modeling for Policy Learning

Learning from demonstration is a powerful method for teaching robots new skills, and more demonstration data often improves policy learning. However, the high cost of collecting demonstration data is a significant bottleneck. Videos, as a rich data source, contain knowledge of behaviors, physics, and semantics, but extracting control-specific information from them is challenging due to the lack of action labels. In this work, we introduce a novel framework, Any-point Trajectory Modeling (ATM), that utilizes video demonstrations by pre-training a trajectory model to predict future trajectories of arbitrary points within a video frame. Once trained, these trajectories provide detailed control guidance, enabling the learning of robust visuomotor policies with minimal action-labeled data. Our method's effectiveness is demonstrated across 130 simulation tasks, focusing on language-conditioned manipulation tasks. Visualizations and code are available at: \url{https://xingyu-lin.github.io/atm}.