受记忆模型启发，构建分层记忆策略提升机器人长时操作能力
MemoAct: Atkinson-Shiffrin-Inspired Hierarchical Memory-Augmented Policy for Robotic Manipulation
- 模仿记忆系统分三层：感知、短期无损、长期压缩
- 在6个任务上实现比基线更高的长程任务成功率
- 适合需要长时间状态追踪的机器人操控场景
记忆增强型机器人策略在处理依赖记忆的任务中至关重要。现有方法通常仅扩展观测窗口，难以同时实现精确的任务状态跟踪与鲁棒的长时程记忆保留。受Atkinson-Shiffrin记忆模型启发，我们提出MemoAct，一种分层记忆增强策略，利用不同层级的记忆解决特定瓶颈：感官记忆过滤即时感知输入，无损短期记忆支持精确任务状态跟踪，压缩长期记忆促进鲁棒的长时程保留。为丰富评估体系，我们基于RoboTwin 2.0构建了MemoryRTBench，包含6个操控任务，系统评估策略在序列、空间和情景记忆三个维度上的表现。在仿真与真实场景中的大量实验表明，MemoAct在性能上优于现有的马尔可夫基线及历史感知策略。
原文摘要 · Abstract (English)
Memory-augmented robotic policies are essential in handling memory-dependent tasks. However, existing approaches typically rely on simply extending the observation window, 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-augmented policy that leverages distinct memory tiers to tackle specific bottlenecks. Specifically, sensory memory filters immediate perceptual inputs, lossless short-term memory supports precise task-state tracking, and compressed long-term memory facilitates robust long-horizon retention. To enrich the evaluation landscape, we construct MemoryRTBench based on RoboTwin 2.0, comprising 6 manipulation tasks that systematically evaluate policy memory capabilities across three dimensions: sequential, spatial, and episodic memory. 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 available at https://tlf-tlf.github.io/MemoActPage/.
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