arXiv AI
Sep 24

MemBodied: Recurrent Associative Memory for Vision-Language-Action Models

MemBodied introduces a fixed‑size episodic memory for Vision‑Language‑Action models, comprising an associative state that tracks interactions across policy calls and an episode anchor that stores a compact representation of the initial scene. By conditioning action generation on these memory components instead of raw past observations, MemBodied reduces context bloat and inference latency. In five memory‑dependent RMBench tasks, it outperforms stateless and vanilla recurrent policies by significant margins, and achieves a 90.6% success rate on the LIBERO‑Long suite, improving over the baseline by 5.4%.

By Tej Deep Pala, Navonil Majumder, Bryce Goh, Raphael Yee, Jianfei Yang, Liming Chen, Soujanya Poria
arXiv Machine Learning
1d ago

When Plans Change, Remember: Replanning-Guided Visual Memory for Vision-Language-Action Models

arXiv:2610.22556v1 Announce Type: cross Abstract: Long-horizon, multi-stage Vision-Language-Action (VLA) policies often require visual information that is no longer present in the current observation...

By Victoria Smelova, Daniil Kazachkov, Georgiy Kazantsev, Iuliia Tsvetkova, Egor Cherepanov, Nikita Kachaev, Artem Latyshev, Aleksandr I. Panov, Alexey K. Kovalev
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