机器人终于有了“记事本”：不再每次失忆
现在的机器人操控模型，每次做动作都像金鱼——只看眼前这一帧，之前发生过什么全忘光。这在“把螺丝拧进刚才那个孔”“按顺序摆好三个杯子”这类依赖历史的任务上，直接抓瞎。这篇论文给机器人装了个固定大小的“记事本”：一个记录跨步骤交互的关联状态，一个保存初始场景的压缩快照。每次决策时，模型只看当前画面加这两个记忆块，而不是把过去所有画面都塞进上下文。结果在需要记忆的任务上，成功率是“失忆版”的7.8倍，比最强的记忆增强方案还高30%，而额外参数只有对方的十分之一。它不是你明天就能装进家里扫地机器人的东西，但“固定大小记忆”这个思路，是让机器人从“会看”走向“会记得”的关键一步。
📄 原文摘要(英文)
Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in recovering this information, but at the significant cost of ever-growing, bloated context and inference latency. We thus introduce MemBodied, a fixed-size episodic memory with two complementary components: an associative state that records interactions across policy calls and an episode anchor that preserves a compact representation of the initial scene as a reference. At each policy call, the model conditions action generation on the current input and the memory components, rather than directly using past observations. Across five evaluated RMBench tasks requiring memory, MemBodied achieves 7.81times the mean success rate of a stateless policy and 2.98times of vanilla recurrent memory, while outperforming the strongest memory-augmented baseline by 1.3times with 10times fewer added parameters. On the fully observable LIBERO-Long suite, it reached 90.6%, a 5.4% improvement over the stateless π_0 policy. These findings support MemBodied as a practical alternative to expanding the policy context for history-dependent manipulation.