机器人有了短期和长期记忆
现在的机器人做长任务时像金鱼——只看当前画面，忘了之前做了什么。这篇让机器人拥有短期和长期两种记忆，且记忆和推理在同一个「思维空间」里流动，不是简单翻历史记录。研究者设计了四个模块：一个管家把经验分存到短期和长期两个仓库；一个搜索员根据当前任务去两个仓库找相关记忆；一个压缩器把找到的记忆浓缩成紧凑的「记忆令牌」;一个编织工把记忆令牌和当前画面、指令混在一起喂给模型。结果是机器人能记住几分钟前拧螺丝的力度，也能回忆昨天学过的开抽屉方式。它不是你明天能用上的，但这是机器人从「看一步做一步」走向「有经验、能规划」的关键一步。
📄 原文摘要(英文)
Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling with long-horizon, temporally dependent tasks. Existing memory-augmented VLAs either expand the observation window or retrieve history from the memory bank as auxiliary policy-side context. However, they leave memory outside the native latent embedding space of VLA reasoning, preventing historical experience from being fluidly interleaved with multimodal reasoning and action formation. To this end, we introduce LaMem-VLA, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning. At its core, LaMem-VLA introduces four coordinated components: (i) a curator that organizes historical experience into two complementary short-term and long-term memory vaults; (ii) a seeker that queries both vaults using the multimodal cognition to retrieve context-relevant evidence; (iii) a condenser that reconstructs the retrieved evidence into compact short-term and long-term latent memory tokens; and (iv) a weaver that injects these memory tokens with the current observation and instruction into one continuous embedding sequence. By representing, retrieving, and consuming historical experience entirely in the same continuous latent space, LaMem-VLA enables memory to directly participate in VLA reasoning and guide action generation under a bounded context. Extensive experiments on SimplerEnv and LIBERO demonstrate the superiority of our LaMem-VLA.