MemBodied: Recurrent Associative Memory for Vision-Language-Action Models

2026-09-23

AI Summary

为解决机器人控制中历史依赖操作任务的信息保留问题，提出MemBodied模型，通过固定大小的情景记忆和条件动作生成方法，显著提高了任务成功率。

Abstract

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 $7.81\times$ the mean success rate of a stateless policy and $2.98\times$ of vanilla recurrent memory, while outperforming the strongest memory-augmented baseline by $1.3\times$ with $10\times$ 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.

Problem

• Vision-Language-Action models
• episode-level information
• history-dependent manipulation
• episodic memory
• inference latency

Innovation

• fixed-size episodic memory
• associative state
• episode anchor