Hierarchical Latent Action Model

2026-03-06

AI Summary

This work addresses the challenge that existing latent action models struggle to capture long-term temporal structures and high-level skills in videos lacking explicit action labels. To overcome this limitation, the paper proposes a Hierarchical Latent Action Model that introduces a novel hierarchical architecture: it leverages a pretrained low-level latent action model to extract fundamental dynamic patterns and employs a sequential aggregation mechanism to automatically discover high-level latent skills. This design enables the model to effectively capture long-range temporal dependencies. Experimental results demonstrate that the proposed approach significantly outperforms current baselines on dynamic skill discovery tasks, exhibiting superior robustness and enhanced capability in modeling long-horizon temporal dynamics.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Latent Action Models
• long-term temporal structure
• hierarchical skills
• actionless video
• temporal dependencies

Innovation

• Hierarchical Latent Action Model
• Long-term Temporal Modeling
• Latent Skill Discovery
• Actionless Video Learning
• Dynamic Pattern Aggregation