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arXiv · 2606.10363

HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation

Xiaoquan Sun·Ruijian Zhang·Chen Cao·Yihan Sun·Jiahui Chen·Zetian Xu·Bo Chen·Haijier Chen·Zhen Yang·Jiarun Zhu·Yijun Hong·JingZhe Xu·Jingrui Pang·Mingqi Yuan·Jiayu Chen
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Abstract

World Action Models (WAMs) have emerged as a new powerful paradigm for embodied intelligence, learning action-relevant visual dynamics that significantly enhance generalization and robustness. However, existing WAMs still struggle with task-relevant memory in long-horizon robotic manipulation. To address this, we present HiMem-WAM, a Hierarchical Memory-Gated WAM that integrates motion-centric latent actions, high-level skill latents, and boundary-triggered memory updates. Specifically, we develop a hierarchical latent action framework that jointly learns low-level motion and high-level skill latents, providing structured temporal abstraction. Meanwhile, a boundary-aware memory gate writes compact task states at predicted skill transitions, enabling causal inference without test-time generation of future video or optical flow estimation. Evaluated on LIBERO, LIBERO-PLUS, RMBench and real-world tasks, HiMem-WAM shows that hierarchical latents improve robustness under deployment perturbations, and the memory module substantially benefits memory-dependent long-horizon manipulation.

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Xiaoquan Sun, Ruijian Zhang, Chen Cao, Yihan Sun, Jiahui Chen, Zetian Xu, Bo Chen, Haijier Chen, Zhen Yang, Jiarun Zhu, Yijun Hong, JingZhe Xu, Jingrui Pang, Mingqi Yuan, Jiayu Chen. 2026-06-09. HiMem-WAM: Hierarchical Memory-Gated World Action Models for Robotic Manipulation. https://arxiv.org/abs/2606.10363

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