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Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Stone Tao · Xiaochen Li · Tongzhou Mu · Zhiao Huang · Yuzhe Qin · Hao Su

Thu Jul 27 04:30 PM -- 06:00 PM (PDT) @ Exhibit Hall 1 #300
Event URL: https://trajectorytranslation.github.io/ »

Training long-horizon robotic policies in complex physical environments is essential for many applications, such as robotic manipulation. However, learning a policy that can generalize to unseen tasks is challenging. In this work, we propose to achieve one-shot task generalization by decoupling plan generation and plan execution. Specifically, our method solves complex long-horizon tasks in three steps: build a paired abstract environment by simplifying geometry and physics, generate abstract trajectories, and solve the original task by an abstract-to-executable trajectory translator. In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate. However, this introduces a large domain gap between abstract trajectories and the actual executed trajectories as abstract trajectories lack low-level details and are not aligned frame-to-frame with the executed trajectory. In a manner reminiscent of language translation, our approach leverages a seq-to-seq model to overcome the large domain gap between the abstract and executable trajectories, enabling the low-level policy to follow the abstract trajectory. Experimental results on various unseen long-horizon tasks with different robot embodiments demonstrate the practicability of our methods to achieve one-shot task generalization.

Author Information

Stone Tao (University of California, San Diego)
Xiaochen Li (University of California, San Diego)
Tongzhou Mu (University of California, San Diego)
Zhiao Huang (UCSD)
Yuzhe Qin (University of California, San Diego)
Hao Su (UCSD)

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