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. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem
The paper introduces FailureSpot, a label‑efficient method for detecting failures at the timestamp level in vision‑language‑action (VLA) policies. It first generates weak supervision from unlabeled VLA action chunks by identifying abnormal patterns, then employs active learning to annotate only the most uncertain trajectories. Experiments on multiple VLA policies demonstrate improved performance for both timestamp‑level and trajectory‑level failure detection.
By Jie Ma, Zongxi Liu, Yi Zhu
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
arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.
By Yi Yu, Xinchuan Qiu
arXiv:2603. 15600v2 Announce Type: replace-cross Abstract: Accurate process supervision remains a critical challenge for long-horizon robotic manipulation.
By Yibin Liu, Yaxing Lyu, Daqi Gao, Zhixuan Liang, Weiliang Tang, Shilong Mu, Xiaokang Yang, Yao Mu