Time-Aware World Model for Adaptive Prediction and Control

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.

Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control

The suspension system is a crucial part of the automotive chassis, improving vehicle ride comfort and isolating passengers from rough road excitation. Unlike passive suspension, which has constant spring and damping coefficients, active suspension incorporates electronic actuators into the system to dynamically control stiffness and damping variables. However, effectively controlling the suspension system poses a challenging task that necessitates real-time adaptability to various road conditions. This paper presents the Physics-Guided Deep Reinforcement Learning (DRL) for adjusting an active suspension system's variable kinematics and compliance properties for a quarter-car model in real time. Specifically, the outputs of the model are defined as actuator stiffness and damping control, which are bound within physically realistic ranges to maintain the system's physical compliance. The proposed model was trained on stochastic road profiles according to ISO 8608 standards to optimize the actuator's control policy. According to qualitative results on simulations, the vehicle body reacts smoothly to various novel real-world road conditions, having a much lower degree of oscillation. These observations mean a higher level of passenger comfort and better vehicle stability. Quantitatively, DRL outperforms passive systems in reducing the average vehicle body velocity and acceleration by 43.58% and 17.22%, respectively, minimizing the vertical movement impacts on the passengers. The code is publicly available at github.com/anh-nn01/RL4Suspension-ICMLA23.