Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation
<|reference_start|>Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation: We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation: interacting with unseen objects in novel scenes without test-time adaptation. While typical approaches rely on a large amount of demonstration data for such generalization, we propose an approach that leverages web videos to predict plausible interaction plans and learns a task-agnostic transformation to obtain robot actions in the real world. Our framework,Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal, and can be trained with diverse videos on the web including those of humans and robots manipulating everyday objects. We use these 2D track predictions to infer a sequence of rigid transforms of the object to be manipulated, and obtain robot end-effector poses that can be executed in an open-loop manner. We then refine this open-loop plan by predicting residual actions through a closed loop policy trained with a few embodiment-specific demonstrations. We show that this approach of combining scalably learned track prediction with a residual policy requiring minimal in-domain robot-specific data enables diverse generalizable robot manipulation, and present a wide array of real-world robot manipulation results across unseen tasks, objects, and scenes. https://homangab.github.io/track2act/<|reference_end|>
@article{bharadhwaj2024track2act:,
    title={Track2Act: Predicting Point Tracks from Internet Videos enables
  Generalizable Robot Manipulation},
    author={Homanga Bharadhwaj, Roozbeh Mottaghi, Abhinav Gupta, Shubham Tulsiani},
    journal={arXiv preprint arXiv:2405.01527},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01527},
    primaryClass={cs.RO cs.CV}
}