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arXiv AI By Riyaaz Shaik, Chandru Venkataraman

REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs

Read the original on arXiv AI →

REFACTOR‑VLA is a wake/sleep framework that learns reusable motor‑program abstractions for vision‑language‑action models. In its sleep phase, it clusters motor‑program fragments using a Behavioral‑Equivalence Kernel derived from rollouts of a latent world model, while its wake phase emits typed lambda terms that a library‑conditioned action decoder consumes. Experiments on LIBERO show that adding an auxiliary supervised contrastive loss during world‑model warm‑up significantly improves clustering quality, outperforming the strongest published baseline across all four task suites.

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 AI.

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