arXiv Computer Vision
UniMem: Unifying Visual and Textual Memory in VLAs for Non-Markovian Tasks
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arXiv Computer Vision
Aug 25
UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models
arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long...
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-...
arXiv Machine Learning
Sep 10
SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
arXiv:2609.05533v1 Announce Type: cross Abstract: Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earl...
arXiv Computer Vision
Oct 2
FlashBack: Knowing When to Remember in Streaming Vision-Language Models
arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between...
arXiv Computer Vision
Sep 3
LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory
LookStep is a new end‑to‑end framework for Vision‑Language Navigation that integrates Language‑Centric Future State Modeling with an Event‑Driven Rolling Memory. It uses language labels to predict coarse navigation progress and future states for candidate actions, and autonomously decides which observations to store in a bounded memory with semantic roles. Empirical results show that LookStep outperforms existing methods on VLN‑CE tasks, achieving a 49.7% success rate on R2R‑CE Val‑Unseen while improving memory efficiency and reducing data requirements.
arXiv Machine Learning
5d ago
When Plans Change, Remember: Replanning-Guided Visual Memory for Vision-Language-Action Models
The paper introduces RePlanMem-VLA, a selective visual memory system for Vision‑Language‑Action models that uses a policy’s own plan revisions as an online memory‑write signal. By detecting unusually large disagreements between temporally aligned action predictions, the method identifies task‑relevant events and updates a bounded visual memory while suppressing redundant frames. Experiments on MIKASA‑Robo‑VLA tasks and real‑robot replay data show that RePlanMem‑VLA retains essential observations in every episode and achieves high ROC‑AUC scores for event detection.