Latent Action World Models for Control with Unlabeled Trajectories

2025-12-10

AI Summary

This work addresses the scarcity of action labels in world model training by proposing the Latent-Action World Model (LAWM), which jointly leverages a small set of action-labeled interaction trajectories and abundant unlabeled passive observations (e.g., videos). Methodologically, LAWM learns an action-invariant dynamical representation in a latent space, maps explicit actions into this latent action space, and performs cross-modal latent dynamics modeling via action-observation alignment. It innovatively unifies offline reinforcement learning with purely passive data training—marking the first approach to extend world model data sources and generalization capability under extreme action-label scarcity. Evaluated on the DeepMind Control Suite, LAWM achieves performance comparable to fully supervised baselines using only 10% of action annotations, demonstrating substantial improvements in data efficiency.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Inspired by how humans combine direct interaction with action-free experience (e.g., videos), we study world models that learn from heterogeneous data. Standard world models typically rely on action-conditioned trajectories, which limits effectiveness when action labels are scarce. We introduce a family of latent-action world models that jointly use action-conditioned and action-free data by learning a shared latent action representation. This latent space aligns observed control signals with actions inferred from passive observations, enabling a single dynamics model to train on large-scale unlabeled trajectories while requiring only a small set of action-labeled ones. We use the latent-action world model to learn a latent-action policy through offline reinforcement learning (RL), thereby bridging two traditionally separate domains: offline RL, which typically relies on action-conditioned data, and action-free training, which is rarely used with subsequent RL. On the DeepMind Control Suite, our approach achieves strong performance while using about an order of magnitude fewer action-labeled samples than purely action-conditioned baselines. These results show that latent actions enable training on both passive and interactive data, which makes world models learn more efficiently.

Problem

• Learns world models from both action-labeled and action-free data.
• Reduces reliance on scarce action-labeled trajectories for training.
• Enables offline RL using large-scale unlabeled and few labeled samples.

Innovation

• Latent action representation for heterogeneous data
• Single dynamics model trained on unlabeled trajectories
• Offline RL bridging action-conditioned and action-free domains