STRAP retrieves semantically meaningful trajectories on large datasets like DROID making it a powerful general-purpose algorithm for robot trajectory retrieval!

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STRAP uses features from vision foundation mode, e.g., DINOv2 as a metric space for the S-DTW matching.

This results in a non-parametric retrieval algorithm that can be used out-of-the-box and naturally scales to larger datasets.

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While behavior cloning memorizes the demos, STRAP learns a much wider distribution through its augmented dataset.

This leads to performance gains of 25% over prior retrieval methods (sim) and 40% over behavior cloning (real)!

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Have some offline data lying around? Use it to robustify few-shot imitation learning! 🤖

STRAP 🎒 is a retrieval-based method that leverages semantic sub-trajectories in offline datasets to augment the training data.

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