机器人终于有了记忆，不再每次都是第一次
现在的机器人操作模型，每看一帧画面都像失忆症患者——它不记得上一秒自己做过什么。这对拿杯子、叠衣服这类需要连续动作的任务是致命的。这篇论文给机器人装了个固定大小的“记忆卡”:一个记录动作间交互的关联状态，一个保存初始场景的锚点。每次决策时，模型只看当前画面加这两块记忆，而不是把过去所有画面都堆在上下文里。结果在需要记忆的任务上，成功率是没记忆模型的7.8倍，比最先进的记忆增强方案还高30%，而新增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，但它指向一个明确方向：机器人要真正干活，得先学会记住。
📄 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.