NEEDLEWORK: Offline Rewriting of Robot Data with Verified Lo
Overview
This paper presents NEEDLEWORK, an offline dataset-augmentation algorithm that improves robot training data by adding verified action bridges between recorded observations in high-dimensional demonstrations. The method uses RGB images, proprioception, and episode-level outcomes to create connections that bypass suboptimal detours, broaden action coverage, and include failed trajectories. It enhances success rates on real-robot tasks by an average of 21 percentage points over strong baselines.
From arXiv Robotics — research abstracts
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- 2026-10-05T04:57:45.653Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsNEEDLEWORK: Offline Rewriting of Robot Data with Verified Local Stitches
This paper presents NEEDLEWORK, an offline dataset-augmentation algorithm that improves robot training data by adding verified action bridges between recorded observations in high-dimensional demonstrations. The method uses RGB images, proprioception, and episode-level outcomes to create connections that bypass suboptimal detours, broaden action coverage, and include failed trajectories. It enhances success rates on real-robot tasks by an average of 21 percentage points over strong baselines.
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