arXiv Machine Learning
Jun 16

AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.

By Lei Xiao, Jifeng Li, Juntao Gao, Feiyang Ye, Yan Jin, Jingjing Qian, Jing Zhang, Yong Wu, Xiaoyuan Yu
arXiv Computer Vision
Aug 27

StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models

StreamPI introduces a streaming multimodal temporal modeling framework that enhances Vision‑Language‑Action models by adding temporal reasoning without extra parameters. It anchors each visual observation and language instruction pair as a temporal unit, using bidirectional attention for cross‑modal fusion and causal attention for autoregressive streaming inference. The method employs random‑interval streaming training to improve robustness and leverages the LLM backbone’s length extrapolation to inherit pretrained weights, achieving superior performance over pi0.5 on real‑robot and simulation tasks.

By Zhe Liu, Jinghua Hou, Yuxiang Lu, Zhenya Yang, Xianzhe Fan, Junwei Luo, Junyi Li, Ruihua Han, Zhi Hou, Hengshuang Zhao
arXiv Computer Vision
Sep 30

EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies

arXiv:2606.20092v3 Announce Type: replace Abstract: Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-...

By Ganlin Yang, Zhangzheng Tu, Yuqiang Yang, Sitong Mao, Junyi Dong, Tianxing Chen, Jiaqi Peng, Jing Xiong, Jiafei Cao, Jifeng Dai, Wengang Zhou, Yao Mu, Tai Wang
arXiv AI
1d ago

Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models

The paper introduces Action–Future Coupled Effect Learning (AFCE), a method that shifts the primary prediction target of vision‑language‑action (VLA) policies from action chunks to future‑grounded physical Effects. AFCE constructs temporally ordered Effect tokens from demonstrations, robot responses, and visual transitions, and uses reconstruction objectives to preserve executable detail and physical change. On the DexJoCo benchmark, AFCE achieves a 50.97% overall success rate, outperforming baseline policies by 11.82 and 15.21 percentage points and excelling on all five bimanual tasks, underscoring the benefit of Effect supervision for coordinated dexterous manipulation.

By Min Lin, Bokai Zhou, Jianqi Lin, Kaiyuan Yang, Yicheng Chang, Junjie Yu, Xiaofan Liu, Jianhua Han, Rui Li, Shen Zhao, Xiaodan Liang
arXiv Computer Vision
Oct 1

D$^2$-VLA: Dual-Memory Dual-Frequency Vision-Language-Action Model For Long Dynamic Manipulation

arXiv:2609.34792v2 Announce Type: replace Abstract: Long-horizon manipulation requires robots to remember cues that are no longer in view while responding to moving objects. Yet vision-language-actio...

By Zijian Ye, Chengqi Wei, Wei Huang, Anlin Zheng, Chunyu Zou, Liangyu Wu, Zikang Zhao, Zhenjie Peng, Yushuo Yang, Shuman Zhao, Zhongrui Wang, Xiaojuan Qi