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.
By Seongheon Park, Wendi Li, Changdae Oh, Samuel Yeh, Zsolt Kira, Michael Hagenow, Sharon Li
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.
By Florian Seligmann, Emiliyan Gospodinov, Enes Ulas Dincer, Gerhard Neumann
The paper introduces VLA‑Tug, a lightweight probe that monitors latent layers of Vision‑Language‑Action models to detect failures early and predict failure modes. By supervising on pre‑failure segments, it improves early detection by up to 30% at matched false‑positive rates and maintains valid missed‑detection and false‑alarm bounds in 86% of runs under unseen task shifts, outperforming existing baselines. The method also raises top‑3 accuracy on unseen tasks by up to 44% and supports real‑time, edge‑efficient monitoring for safer deployment.
By Michal P. Podolinsky, Yunhao Yang, Daniel Little, Hong Lu, Rohan Siva, Olin Goudey, Neel P. Bhatt, Matthias Scheutz, Ufuk Topcu
The paper introduces Configured Failure Trapping, a new backdoor attack targeting Vision‑Language‑Action (VLA) models that activates through subtle textual triggers and forces the robot to fail in a specific, controlled manner. It presents a data engine for generating high‑quality target trajectories, an automated evaluation suite, and two benchmarks—Trap‑LIBERO and Trap‑RoboTwin—covering four failure modes. The authors propose TrapVLA, a method that learns trigger‑induced action residuals to steer policies toward the desired failure behavior, demonstrating effectiveness in both simulation and real‑world robotic experiments while maintaining performance on clean data.
By Jun-Hui Liu, Kun-Yu Lin, Yi-Lin Wei, Xu-Han Chen, Yinghao Li, Zhuohao Li, Yuan-Ming Li, Qing Zhang, Xiaoyi Fan, Dongmei Jiang, Yan Li, Wei-Shi Zheng
ProTracer is a training‑free framework that uses Vision‑Language Models (VLMs) together with proprioceptive signals to analyze robot manipulation failures. It performs binary failure detection, categorization, explanation generation, and introduces failure onset localization—identifying the earliest moment a robot deviates from a valid trajectory leading to failure. The method leverages proprioceptive dynamics to pinpoint informative action boundaries and converts robot‑state signals into natural‑language descriptions for joint multimodal reasoning, achieving strong performance on both conventional failure diagnosis and the new failure onset localization task.
By Chang Dong, Mehdi Hosseinzadeh, King Hang Wong, Lingqiao Liu, Francois Fraysse, Feras Dayoub, Minh Hoai Nguyen
arXiv:2512.01946v4 Announce Type: replace-cross
Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in r...
By Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid