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arXiv Machine Learning By Minkyu Kim, Junhoo Lee, Seungyeon Kim, Suhyun Jeon, Baekseung Kim, Donghoon Han, Nojun Kwak

Policy-Aligned Predictive Verification for Adaptive VLA Action-Chunk Execution

Read the original on arXiv Machine Learning →

The paper introduces a policy‑aligned predictive verifier (PAV) that evaluates candidate action prefixes from vision‑language‑action (VLA) policies to determine how far to execute before re‑planning. PAV predicts the consequences of prefixes and checks their compatibility with successful task completion, using trajectory‑level survival supervision and a localization prior to learn local judgments. Experiments on LIBERO, RoboTwin 2.0, and real‑world manipulation demonstrate that PAV improves the trade‑off between success rate and query cost compared to existing adaptive methods.

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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