arXiv Computer Vision
EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
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Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
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UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models
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AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
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LAVE: Latent Visual Evidence-Enhanced Planning for Video Tool-use Agents
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Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation
Mem-World introduces a memory‑augmented action‑conditioned world model for robot manipulation, featuring W‑VMem—a 4D wrist‑view‑centered surfel‑indexed memory that anchors historical observations to evolving surface elements. By explicitly modeling when and where scene elements are observed, the system retrieves geometry‑aware history frames during generation, providing informative, non‑redundant context for future action predictions. Experiments demonstrate that Mem‑World produces persistent rollouts, improves policy evaluation reliability (14.5 % higher Pearson correlation with real‑world performance), and boosts long‑horizon task success rates from 58 % to 72 % using synthetic data generation.
arXiv Computer Vision
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StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
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