Time-Aware World Model for Adaptive Prediction and Control

• URL: http://arxiv.org/abs/2506.08441v1
• Date: Tue, 10 Jun 2025 04:28:11 GMT
• Title: Time-Aware World Model for Adaptive Prediction and Control
• Authors: Anh N. Nhu, Sanghyun Son, Ming Lin,
• Abstract summary: Time-Aware World Model (TAWM) is a model-based approach that explicitly incorporates temporal dynamics.<n>TAWM learns both high- and low-frequency task dynamics across diverse control problems.<n> Empirical evaluations show that TAWM consistently outperforms conventional models.
• Score: 20.139507820478872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, {\Delta}t, and training over a diverse range of {\Delta}t values -- rather than sampling at a fixed time-step -- TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system's underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at: github.com/anh-nn01/Time-Aware-World-Model.

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