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
arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.
By Bingjia Huang, Xiangyu Li, Xiang Wang, Liang Mi, Zixu Hao, Weijun Wang, Hao Wu, Kun Li, Yunxin Liu, Ting Cao
VLA-Scope is a two‑stage framework designed to predict failures in vision‑language‑action models under distribution shifts. The first stage detects out‑of‑distribution inputs and classifies their shift categories using pooled image and language representations. For OOD inputs, the second stage updates failure risk during execution by combining shift category, action‑prefix features, and execution progress, achieving a ROC‑AUC of 0.8497 after 60 actions and outperforming baseline methods.
By Kaiwen Zhu, Dongfang Liu, Liangkai Liu
FailBench is a new benchmark for robot failure detection, containing 2,197 manipulation attempts from 14 public sources, with 75% of failures occurring naturally. The study evaluates 13 vision‑language model (VLM) detectors, finding the best model achieves only 0.77 mean balanced accuracy, and that fine‑tuned failure detectors often underperform general‑purpose VLMs. Performance varies with visual evidence, excelling when object motion is observable but dropping to near chance on contact‑intensive assembly tasks, and input‑level cropping of outcome‑relevant regions improves the top detector by 2.4 percentage points.
By Zaruhi Navasardyan, Tatul Danielyan, Hrant Davtyan