VLA‑Δ introduces a framework that explicitly learns physical state transitions for vision‑language‑action models, using changes between current and future states as learning targets. The authors build a large‑scale pretraining dataset with physical state‑transition annotations that cover task‑relevant semantics and visual motion changes, providing direct supervision for continuous action generation. The resulting model performs competitively on LIBERO and RoboTwin benchmarks and outperforms π₀.₅ in real‑robot experiments, while requiring no extra inference overhead.
By Qi Lu, Jiarun Li, Ce Zhang, Miao Pan, Yi Pan, Xu Wang, Wenqi Zhang, Xuhong Zhang
arXiv:2605. 22183v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation.
By Weilong Guo, Yuchen Wang, Renping Zhou, Yunfeng Zhang, Rui Fang, Yuyang Pang, Wenda Xu, Gao Huang
V-Link is a method designed to enhance Vision‑Language‑Action (VLA) models by recovering visual representations during the transfer from vision‑language (VL) features to action (A) features. It introduces complementary Spatial and Semantic Query representations that are injected into Action DiT through asymmetric pathways, providing both semantic augmentation and dedicated geometric conditioning for action generation. Experiments on LIBERO, LIBERO‑Plus, RoboTwin 2.0, and real‑world AGIBOT A3 Ultra tasks show significant performance gains over the base GR00T N1.6 model.
By Yehao Lu, Jiarui Yang, Yuning Su, Yufeng Xie, Yu Zhong, Yazhou Zhang, Haiyu Lan, Kaixiang Lu, Peiwen Lin, Chuang Wang, Zequn Qin, Enyu Li, Xi Li
Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness. We present NebulaVLA...
arXiv:2610.33685v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models are bottlenecked by the scarcity of robot data. Egocentric human manipulation videos offer an abundant, diverse,...
By Qingyuan Cai, Bo Zhang, Yongzhen Huang, Jianyu Chen
arXiv:2608. 16503v1 Announce Type: cross Abstract: Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness.
By Cong Zhao, Shuai Tian, Xu Zhang, Baocheng Ni, Xinguo Song, Xueying Sun, Shu Jiang, Shouchang Yang, Bo Tang, Jin Deng, Ge Zhu, YongCheng Wang, Jin Xu, Ri Yang
arXiv:2606. 27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation.
By Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao
arXiv:2607. 13597v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models inherit rich semantic representations from pretrained Vision-Language Models, yet fine-tuning on limited robot demonstrations degrades this structure and undermines generalization.
By Yuan Xu, Youheng Shi, Chengyang Li, Wentao Zhu, Yizhou Wang
arXiv:2610.08133v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token seq...
By Owen Du, Yang Yue, Jie Zhang, Jiaqi Pi, Chi Bene Chen, Gao Huang
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models benefit from large-scale pretraining, their predominantly static pretraining objectives provide limited supervision for physical dynamics and temporal causality, leaving control-relevant knowledge to be learned from downstream robot demonstrations.
The paper introduces 2AM, a system that separates memory and action execution in long‑horizon robot manipulation. 2AM stores task memory exclusively in a multimodal Agent, while a single RGB‑based, stateless Action Model performs motion based on language and optional 2D hints. On the LIBERO‑Mem benchmark, 2AM achieves 76.3% average completion—over 61 points higher than the best baseline—demonstrating that agent‑side memory and precise steering of the Action Model can substantially improve performance.
By Yutong Hu, Fengjiao Chen, Xuezhi Cao, Renaud Detry
arXiv:2609.06079v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hier...
By Zheng Lu, Haoran Liao, Wanqi Zhong, Yunhe Ni, Lijie Wang, Xingjie Fan, Zhisheng Chen, Yantang Qu, Meijia Chen, Tianyu Xin, Zirui Song, Yiming Li