arXiv AI
SentryVLA: Uncertainty-Guided Action Selection and State Monitoring for Vision-Language-Action Robotic Control
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arXiv Machine Learning
Oct 12
VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models
VLA-FAIL introduces a lightweight framework for detecting failures in vision-language-action models used in robotic manipulation. It combines two novel detectors: the first-layer Mahalanobis distance (LLMD) identifies out-of-distribution states by comparing token-wise deviations in last-layer features to training data, while the second, action chunk consistency (ACC), detects failures by spotting inconsistencies between consecutive action chunks in receding-horizon control. The authors also propose a threshold‑independent metric, “AUCPDT,” to evaluate detection accuracy and latency, and show through real‑world and simulation experiments that LLMD and ACC together provide reliable and early failure detection, outperforming more expensive baseline methods.
arXiv AI
Oct 23
PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
PaceVLA introduces adaptive temporal action quantization for Vision‑Language‑Action models, enabling faster robot execution by aggregating low‑level actions into coarser steps when appropriate. The framework combines mode‑specific action decoders with a quantizability‑guided mode selection and a context‑conditioned router to decide execution granularity at each inference step. Experiments on simulation benchmarks and real‑world tasks demonstrate that PaceVLA improves execution efficiency while preserving task success rates across diverse embodiments.
arXiv Machine Learning
Sep 10
LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction
arXiv:2608.25757v4 Announce Type: replace-cross Abstract: Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: a...
arXiv AI
Sep 28
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
The paper introduces Hide-and-Seek, a framework for detecting failures in Vision‑Language‑Action (VLA) models during robot execution. It treats failure detection as a coarsely supervised learning problem, using inter‑trajectory and intra‑trajectory contrastive objectives to localize failure‑indicative actions without step‑level annotations. Experiments on LIBERO, VLABench, and a real‑world robotic platform show that Hide‑and‑Seek achieves state‑of‑the‑art multi‑task failure detection performance across several VLA policies.
Hugging Face Trending Papers
Oct 19
PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
Fast and reliable manipulation is crucial for practical robot deployment. Although Vision-Language-Action (VLA) models have recently shown remarkable progress as generalist robot policies, their execu...
arXiv Machine Learning
Jun 16
AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.