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.

翻译：预训练的视觉-语言-动作模型在提升端到端机器人操作的鲁棒性和泛化能力方面取得了显著成功。然而，由于缺乏记忆机制且仅依赖即时感知输入，这些模型在处理长时程任务时面临困难。为克服这一局限，我们提出记忆增强提示的视觉-语言-动作模型，这是一种创新框架，通过从示范数据衍生的记忆提示增强预训练VLA模型，以提升长时程机器人操作任务的动作生成能力。为实现这一目标，MAP-VLA首先从历史示范数据构建记忆库，其中每个记忆单元捕获任务特定阶段的信息。这些记忆单元以实现为可通过提示调优优化的可学习软提示。随后，在实时任务执行过程中，MAP-VLA通过轨迹相似性匹配检索相关记忆，并动态将其集成到VLA模型中，以增强动作生成。重要的是，这种提示调优与检索增强方法可作为即插即用模块作用于冻结的VLA模型，为提升任务性能提供轻量级且灵活的解决方案。实验结果表明，在长时程任务中，MAP-VLA在仿真基准测试中实现最高7.0%的绝对性能提升，在真实机器人评估中达到25.0%的提升，超越了当前最先进的方法。