Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation
| Autor: | Bharadhwaj, Homanga, Mottaghi, Roozbeh, Gupta, Abhinav, Tulsiani, Shubham |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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/ Comment: ECCV 2024. Last 3 authors contributed equally |
| Databáze: | arXiv |
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