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arXiv · 2611.02660

HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation

Gyeonghun Kim·Donghoon Kim·Minsoo Choi·Suyun Lee
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Abstract

Recent advances in Vision-Language-Action (VLA) models have enabled fine manipulation capabilities, yet performing tasks that require working memory remains a key challenge. Most existing VLA architectures operate under a Markovian assumption, mapping only the current observation to an action, which leads to failure in non-Markovian environments. Inspired by the human 'inner monologue', we propose Hierarchical Explicit Language Memory (HeLM), a hierarchical VLA framework that maintains task-relevant history in explicit language through factorized event detection and event-triggered memory updates. We evaluate HeLM across 10 non-Markovian real-world tasks with 56 variants, including five newly formulated inter-episode tasks that require persistent context across task boundaries. HeLM improves intra-episode success by 12.8 percentage points over the strongest baseline and achieves 78% subtask success on compositional generalization tasks, while using up to 4.5x less memory and running 2.5x faster than the keyframe baseline at long horizons.

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Gyeonghun Kim, Donghoon Kim, Minsoo Choi, Suyun Lee. 2026-11-02. HeLM: Hierarchical Explicit Language Memory for Non-Markovian Manipulation. https://arxiv.org/abs/2611.02660

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