arXiv 2511.095162025-11-12cs.ROcs.CV

MAP-VLA：用于机器人操作中视觉-语言-动作模型的记忆增强提示

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

Nanyang Technological University（南洋理工大学）；VinUniversity（文大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；South China University of Technology（华南理工大学）

Runhao Li, Wenkai Guo, Zhenyu Wu, Changyuan Wang, Haoyuan Deng, Zhenyu Weng, Yap-Peng Tan, Ziwei Wang

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AI总结：

针对预训练视觉-语言-动作（VLA）模型在长程机器人操作任务中因缺乏记忆表现不佳的问题，提出MAP-VLA框架，通过演示衍生的记忆提示增强动作生成，在仿真和真实机器人评估中分别取得7.0%和25.0%的性能提升。

摘要：

预训练的视觉-语言-动作（VLA）模型在提升端到端机器人操作的鲁棒性和泛化性方面取得了显著成功。然而，这些模型因缺乏记忆且仅依赖即时感官输入，在长程任务中表现不佳。为解决这一局限，我们提出用于视觉-语言-动作模型的记忆增强提示（MAP-VLA）——一种新框架，通过演示衍生的记忆提示为预训练VLA模型赋能，以增强长程机器人操作任务的动作生成。具体而言，MAP-VLA首先从历史演示中构建记忆库，每个记忆单元捕捉任务特定阶段的信息，这些单元以可学习的软提示形式实现，通过提示调优优化。实时任务执行时，MAP-VLA通过轨迹相似性匹配检索相关记忆，并将其动态整合到VLA模型中以增强动作生成。重要的是，这种提示调优和检索增强方法作为即插即用模块作用于冻结的VLA模型，提供轻量灵活的解决方案以提升任务性能。实验结果显示，MAP-VLA在仿真基准中实现了高达7.0%的绝对性能提升，在真实机器人长程任务评估中提升25.0%，优于当前最先进方法。

英文摘要：

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.

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