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arXiv · 2603.05815

Hierarchical Latent Action Model

Hanjung Kim·Lerrel Pinto·Seon Joo Kim
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Abstract

Latent Action Models (LAMs) enable learning from actionless data for applications ranging from robotic control to interactive world models. However, existing LAMs typically focus on short-horizon frame transitions and capture low-level motion while overlooking longer-term temporal structure. In contrast, actionless videos often contain temporally extended and high-level skills. We present HiLAM, a hierarchical latent action model that discovers latent skills by modeling long-term temporal information. To capture these dependencies across long horizons, we utilize a pretrained LAM as a low-level extractor. This architecture aggregates latent action sequences, which contain the underlying dynamic patterns of the video, into high-level latent skills. Our experiments demonstrate that HiLAM improves over the baseline and exhibits robust dynamic skill discovery.

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Hanjung Kim, Lerrel Pinto, Seon Joo Kim. 2026-03-06. Hierarchical Latent Action Model. https://arxiv.org/abs/2603.05815

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