The paper introduces Reasoning and Flow Memory (FLOWMEM), a vision‑language‑action model that reuses successful latent reasoning steps instead of discarding them after each policy query. FLOWMEM dynamically retrieves and recomposes compatible latent fragments to form a reasoning route that aligns with the temporal structure of prior successful computations, then refines this route with current visual and proprioceptive data before generating actions. Experiments on RoboMME and LIBERO‑Plus show that FLOWMEM improves success rates by 1.7 and 4.1 percentage points over memory‑free policies, demonstrating the benefit of reusing latent computation for closed‑loop control.
By Hongyu Shi, Sen Zhao, Zuyu Zhang, Lifeng Shen, Ding Zou, Xinyu He, Xu Zhang, Qinghua Zhang
arXiv:2610.14406v1 Announce Type: cross
Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past...
By Yilin Wu, Rutav Shah, Shuaiyi Huang, Sumedh A. Sontakke, Homanga Bharadhwaj, Andrea Bajcsy, Max Simchowitz, Guanya Shi
arXiv:2609.22684v1 Announce Type: cross
Abstract: Memory-dependent robotic manipulation often requires later actions to use information from earlier interactions. Existing vision-language-action (VLA...
By Wenzhuo Li, Qiongfeng Shi, Yi Zhou
arXiv:2603. 04910v2 Announce Type: replace-cross Abstract: Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single-step observations or short-context histories, making them struggle with non-Markovian tasks that require long-term memory.
By Yuheng Lei, Zhixuan Liang, Hongyuan Zhang, Ping Luo
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:2609.36595v1 Announce Type: cross
Abstract: Visual-memory systems commonly retain or compress past observations. Robot control additionally requires interaction-derived state that no individual...
By Yuyou Zhang, Yunbei Zhang, Miao Li, Janet Wang, Zijian Jin, Shilong Liu, Ding Zhao
UniWALM is a unified world-action-language model that jointly learns video, action, and language in a single shared representation, using a mixture-of-transformers backbone with diffusion heads that co-denoise across modalities. By generating language as part of the same pass as action, the model closes a language‑as‑memory loop without an external planner, and introduces Flow Matching with Correction to maintain reliable action streams. Experiments show UniWALM outperforms baselines on compositional pick‑and‑place tasks and memory‑dependent benchmarks, and dramatically improves success on a real‑world Find‑the‑Item task when using generated textual memory.
By Rahul Sajnani, Diego Romeres, Mingyuan Zhang, Kai Gao, Carl Qi, Baichuan Huang, Srinath Sridhar, Dylan Randle, Lillian J. Ratliff, Jose Barreiros, Manikantan Nambi, Jeroen Vanbaar
arXiv:2608.29537v1 Announce Type: cross
Abstract: Frozen vision-language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so t...
By Hongbo Gao, Zeyu Ni, Xin Wen, Siyu Xu, Ruifeng Li
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: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:2610.10528v1 Announce Type: cross
Abstract: Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Lon...
By Wei Huang, Bohan Zhang, Chenzhi Liu, Isabella Liu, Shuai Yang, Weian Mao, Luozhou Wang, Yicheng Xiao, Weifeng Lin, Qixin Hu, Bryan Chu, Sifei Liu, Linxi Fan, Xiaojuan Qi, Song Han, Yukang Chen
arXiv:2608. 03701v1 Announce Type: cross Abstract: World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve.
By Fan Yang, Yuting Su, Xiaobo Wang, Yuncheng You, Fugui Fan, Yuting Wu, Minghui Wu, Chenxu Zhao, JiaHong Ning, Peiguang Jing