# MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation

arXiv: https://arxiv.org/abs/2511.09516 · HTML: https://mlsanity.com/paper/2511.09516/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning and chain-of-thought in VLA, one of 13 directions in Vision-language-action models.
- Builds on 19 of its 22 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Placed in

- Vision-language-action models / [Reasoning and chain-of-thought in VLA](https://mlsanity.com/vla/reasoning-and-chain-of-thought-in-vla/)

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models](https://mlsanity.com/paper/2503.22020/) (2025)
- [3D-VLA: A 3D Vision-Language-Action Generative World Model](https://mlsanity.com/paper/2403.09631/) (2024)
- [Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets](https://mlsanity.com/paper/2109.13396/) (2021)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model](https://mlsanity.com/paper/2311.18231/) (2023)
- [In-Context Imitation Learning via Next-Token Prediction](https://mlsanity.com/paper/2408.15980/) (2024)
- [Instant Policy: In-Context Imitation Learning via Graph Diffusion](https://mlsanity.com/paper/2411.12633/) (2024)
- [In-Context Learning Enables Robot Action Prediction in LLMs](https://mlsanity.com/paper/2410.12782/) (2024)
- [InCoRo: In-Context Learning for Robotics Control with Feedback Loops](https://mlsanity.com/paper/2402.05188/) (2024)

## Built on by (most cited first, 28 of 28)

- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection](https://mlsanity.com/paper/2604.13942/) (2026)
- [μVLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models](https://mlsanity.com/paper/2606.12497/) (2026)
- [From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model](https://mlsanity.com/paper/2605.22671/) (2026)
- [NativeMEM: Native Memory Compression for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2607.06678/) (2026)
- [Chameleon: Control-Indexed Prospective Memory for Visuomotor Manipulation](https://mlsanity.com/paper/2603.24576/) (2026)
- [LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories](https://mlsanity.com/paper/2606.13578/) (2026)
- [SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models](https://mlsanity.com/paper/2609.05533/) (2026)
- [PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning](https://mlsanity.com/paper/2602.20323/) (2026)
- [ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models](https://mlsanity.com/paper/2605.10993/) (2026)
- [WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation](https://mlsanity.com/paper/2606.17463/) (2026)
- [SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation](https://mlsanity.com/paper/2608.05970/) (2026)
- [TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation](https://mlsanity.com/paper/2606.14551/) (2026)
- [HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control](https://mlsanity.com/paper/2607.03449/) (2026)
- [AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models](https://mlsanity.com/paper/2608.06729/) (2026)
- [Think Only When Needed: Prompt-Authority Control for Selective Slow-Path Intervention in Vision-Language-Action Manipulation](https://mlsanity.com/paper/2608.23224/) (2026)
- [UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling](https://mlsanity.com/paper/2609.11875/) (2026)
- [TraceFlow: Guiding Frozen Flow-Matching Robot Policies with Success and Failure Traces](https://mlsanity.com/paper/2609.20646/) (2026)
- [MemBodied: Recurrent Associative Memory for Vision-Language-Action Models](https://mlsanity.com/paper/2609.28256/) (2026)
- [AnchorVLA4D: an Anchor-Based Spatial-Temporal Vision-Language-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2603.12730/) (2026)
- [RoboMME-Interference: Benchmarking Robot Memory Under Interference](https://mlsanity.com/paper/2606.22338/) (2026)
- [Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval](https://mlsanity.com/paper/2607.00379/) (2026)
- [Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models](https://mlsanity.com/paper/2608.04765/) (2026)
- [Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory](https://mlsanity.com/paper/2608.15269/) (2026)
- [Reuse Before You Retrieve: Diagnosing Headroom and Complementarity for Test-Time Augmentation of Embodied Multimodal Policies](https://mlsanity.com/paper/2608.17484/) (2026)
- [Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI](https://mlsanity.com/paper/2609.04552/) (2026)
- [CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoning](https://mlsanity.com/paper/2609.33007/) (2026)
- [Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model](https://mlsanity.com/paper/2609.39794/) (2026)

## Abstract (arXiv)

Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these models struggle with long-horizon tasks due to their lack of memory and reliance solely on immediate sensory inputs. To address this limitation, we propose Memory-Augmented Prompting for Vision-Language-Action model (MAP-VLA), a novel framework that empowers pre-trained VLA models with demonstration-derived memory prompts to augment action generation for long-horizon robotic manipulation tasks. To achieve this, MAP-VLA first constructs a memory library from historical demonstrations, where each memory unit captures information about a specific stage of a task. These memory units are implemented as learnable soft prompts optimized through prompt tuning. Then, during real-time task execution, MAP-VLA retrieves relevant memory through trajectory similarity matching and dynamically integrates it into the VLA model for augmented action generation. Importantly, this prompt tuning and retrieval augmentation approach operates as a plug-and-play module for a frozen VLA model, offering a lightweight and flexible solution to improve task performance. Experimental results show that MAP-VLA delivers up to 7.0% absolute performance gains in the simulation benchmark and 25.0% on real robot evaluations for long-horizon tasks, surpassing the current state-of-the-art methods.