[Submitted on 1 Oct 2026]
Title:NEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches
View a PDF of the paper titled NEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches, by Juntao Ren and 2 other authors
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Abstract:Robot demonstrations may contain useful behavior even when individual episodes are inefficient or unsuccessful. Trajectory stitching offers a way to compose these behaviors into improved training data, but identifying useful connections and verifying their feasibility is difficult in high-dimensional robot data, where many prior methods rely on low-dimensional state representations. We introduce NEEDLE, an offline dataset-augmentation algorithm that addresses these challenges by adding short, verified action bridges between recorded observations in high-dimensional robot demonstrations. First, NEEDLE identifies and creates connections that bypass suboptimal detours, broaden action coverage, and augment the original dataset with failed trajectories, using only RGB images, proprioception, and episode-level outcomes, without new environment interaction or privileged object state. Next, we present a sampling technique that incorporates accepted bridges into policy training without synthesizing intermediate images or discarding the original demonstrations, allowing policies to learn alternative actions while retaining the original dataset's coverage. On real-robot tasks, NEEDLE improves success rate over the strongest baseline on each task by an average of 21 percentage points. Videos and supplementary materials are on this https URL.
Comments: Submitted to ICLR 2027. Project page: this https URL
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2610.02339 [cs.RO]
(or arXiv:2610.02339v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.02339
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Juntao Ren [view email] [v1] Thu, 1 Oct 2026 18:11:32 UTC (7,986 KB)
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