World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
Year: 2026
Type: preprint
Abstract: World models are emerging as a key interface between perception and decision making in embodied intelligence, enabling agents to preserve hidden state, anticipate consequences, compare interventions, and adapt when execution departs from expectation. Much of this progress has been measured by visual fidelity. For an embodied agent, however, the purpose of a world model is to improve behavior by predicting futures that inform intervention and decision making. Before reaching for a cup, a person anticipates its weight and how it will resist the grasp, and the hand shapes itself before contact. That anticipation is coarse and rarely pictorial, yet it guides the movement. The key question is therefore which properties of a prediction let it improve behavior. Existing surveys organize this literature by architecture, output modality, or application domain, which leaves this question implicit. We accordingly characterize world model capability through three progressively stronger levels: A Plausible world model preserves task-relevant temporal, geometric, or physical structure; a Controllable model additionally predicts how interventions change that structure; and an Actionable model uses prediction to produce measurable improvements in planning, action, learning, evaluation, verification, recovery, or data selection. We complement this capability ladder with a 3x4 analysis matrix that crosses geometry, physics, and action grounding with data, reward, policy, and model-self improvement loops. Using this framework, we survey research across manipulation, navigation, locomotion, autonomous driving, and general embodied learning. We organize methods as technical progressions, clarify the capability required at each level, and examine datasets, benchmarks, and evaluation protocols. Finally, we identify open challenges in long-horizon consistency, uncertainty calibration, causal intervention testing, latency, verification and recovery, and cross-embodiment transfer. This perspective motivates a shift in evaluation from whether a model can generate a convincing future to whether it represents the right state, responds correctly to action, and improves the closed-loop behavior of an embodied agent.
Source: Preprints.org
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212293299