SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models

arXiv:2609.05533 · cs.CV, cs.LG, cs.RO · Submitted 2026-09-02 · Read on arXiv

cs.CV, cs.LG, cs.RO

Submitted: 2026-09-02

Updated: 2026-09-25

Comments: 29 pages, 12 figures

Code: https://github.com/wadeKeith/SimpleMemVLA

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 35× in this paper · explained in 2 episodes
• Decode — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• In-Context Learning — 4× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Cross-Embodiment Transfer — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Qwen3-VL Technical Report
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• WorldVLA: Towards Autoregressive Action World Model
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
• Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
• RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• Gated Memory Policy: In-Context Memorization and Adaptation
• Ctrl-World: A Controllable Generative World Model for Robot Manipulation
• ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
• ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation
• NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
• HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
• RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
• RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Interactive Environmental Learning in Physical Embodied Systems
• MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation

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