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
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
Oct 2

Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies

The paper introduces Divide-and-Remember (D&R), a recursive memory method for vision-language-action (VLA) policies that optimises memory by maximizing the conditional mutual information between actions and memory given observations. D&R recursively divides the full history into top‑K selections over 2K tokens, using a shared lightweight selector across all recursion blocks to handle unbounded histories efficiently. Evaluated on the RoboMME benchmark of 16 long‑horizon manipulation tasks, D&R achieves state‑of‑the‑art success rates with consistent gains across all suites while using only 64 tokens, and similar improvements are observed in real‑robot experiments.

By Xuehui Yu, Eason Yu, Meiyi Wang, Haozhe Du, Stefano V. Albrecht, Harold Soh