MemoAct: Atkinson-Shiffrin-Inspired Memory-Augmented Visuomotor Policy for Robotic Manipulation

• URL: http://arxiv.org/abs/2603.18494v1
• Date: Thu, 19 Mar 2026 05:02:43 GMT
• Title: MemoAct: Atkinson-Shiffrin-Inspired Memory-Augmented Visuomotor Policy for Robotic Manipulation
• Abstract summary: MemoAct is a hierarchical memory-based policy that leverages distinct memory tiers to tackle specific bottlenecks.<n>MemoAct achieves superior performance compared to both existing Markovian baselines and history-aware policies.
• Score: 6.490934654648497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory-augmented robotic policies are essential in handling memory-dependent tasks. However, existing approaches typically rely on simple observation window extensions, struggling to simultaneously achieve precise task state tracking and robust long-horizon retention. To overcome these challenges, inspired by the Atkinson-Shiffrin memory model, we propose MemoAct, a hierarchical memory-based policy that leverages distinct memory tiers to tackle specific bottlenecks. Specifically, lossless short-term memory ensures precise task state tracking, while compressed long-term memory enables robust long-horizon retention. To enrich the evaluation landscape, we construct MemoryRTBench based on RoboTwin 2.0, specifically tailored to assess policy capabilities in task state tracking and long-horizon retention. Extensive experiments across simulated and real-world scenarios demonstrate that MemoAct achieves superior performance compared to both existing Markovian baselines and history-aware policies. The project page is \href{https://tlf-tlf.github.io/MemoActPage/}{available}.

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