← Back to all news
arXiv AI By Minsu Jang, Jae-Woo Choi, Youngwoo Yoon, Dohyung Kim

VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression

Read the original on arXiv AI →

The study investigates how vision‑language‑action (VLA) policies behave when given no, meaningless, or infeasible instructions. Across six model checkpoints, robots often began moving and completed unintended tasks even with empty or nonsensical prompts. Various suppression techniques—prefixing a stop instruction, gating based on action similarity, and fine‑tuning—showed limited success in preventing unwanted motion or task execution.

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.

Related stories

arXiv Machine Learning
Oct 2

When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models

The paper investigates a specific failure mode in vision‑language‑action models, termed instruction‑action binding, where models respond to language and vision separately but fail to combine them to select the correct action under counterfactual changes. Through behavioral analyses of fine‑tuned policies, the authors show that failed rollouts often preserve source behavior or switch to other demonstrated tasks, indicating that language is not ignored but mis‑bound. They propose Equivariant Counterfactual Training (ECT), which supplies counterfactual demonstrations and a paired loss to enforce correct action selection, achieving significant performance gains across simulated and real‑world benchmarks.

By Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen, Yan-Fu Chen, Binghua Cai, Min Sun, Chun-Yi Lee
fine-tuningmultimodal
More like this →
arXiv AI
Oct 27

What Can One Sentence Fix? A Failure Taxonomy and a Minimal Language Correction for Frozen VLA Policies

The paper investigates whether a single descriptive sentence can correct failures in frozen vision‑language‑action (VLA) policies. By labeling failures in the LIBERO‑90 benchmark, the authors identify four failure modes, finding that only execution misalignment—where the policy reaches the correct target but misjudges distance or force—can be remedied with a concise human‑generated sentence. This sentence, parsed by an LLM into a bias on the policy’s actions, successfully restores performance on both simulated and real‑world tasks, while automated language models fail to achieve similar gains.

By Owen Kwon, Pablo Ortega-Kral, Arthur Bucker, Jean Oh
llmsroboticsfine-tuningmultimodalsafety
More like this →