arXiv:2601. 04266v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics.
By Ji Guo, Wenbo Jiang, Yansong Lin, Yijing Liu, Ruichen Zhang, Guomin Lu, Aiguo Chen, Xinshuo Han, Hongwei Li
arXiv:2609.39178v1 Announce Type: cross
Abstract: Recently, Vision-Language-Action (VLA) models have revolutionized robotic manipulation by seamlessly integrating visual perception, language understa...
By Songhua Yang, Ziyu Liu, Yuanwei Liu, Xuetao Li, Xuanye Fei, He Huang, Zheng Wang, Miao Li
arXiv:2608. 10393v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in controlling robots across diverse manipulation tasks.
By Jiahui Han, Yuhui Yao, Xin Wang, Jiafei Cao, Mingxuan Zhang, Danfeng Shan, Huiqi Deng, Guanchu Wang, Xia Hu
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
arXiv:2609.39047v1 Announce Type: new
Abstract: Interactive video generation (IVG) models have achieved remarkable progress in producing controllable visual content guided by user-defined actions, ye...
By Zhihang Wu, Zhongqi Wang, Jie Zhang, Fengming Gu, Shiguang Shan, Xilin Chen
arXiv:2607. 11063v1 Announce Type: new Abstract: Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances.
By Chenyang Li, Kaige Li, Zeyu Jiang, Changhao Chen