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arXiv Machine Learning By Edgar Welte, Yitian Shi, Rosa Wolf, Maximillian Gilles, Rania Rayyes

FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation

Read the original on arXiv Machine Learning →

FlowCorrect is a modular interactive imitation learning method that allows real‑time adaptation of generative flow‑matching manipulation policies using sparse, relative human corrections. During task execution, a human provides brief corrective pose nudges through a lightweight VR interface, and FlowCorrect locally adapts the policy without retraining the backbone, maintaining performance on previously learned scenarios. Experiments on a real‑world robot across four tabletop tasks show that, with a low correction budget, FlowCorrect achieves an 80% success rate on previously failed cases while preserving performance on solved scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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