World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

• URL: http://arxiv.org/abs/2512.03429v1
• Date: Wed, 03 Dec 2025 04:15:31 GMT
• Title: World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations
• Authors: Raul Steinmetz, Fabio Demo Rosa, Victor Augusto Kich, Jair Augusto Bottega, Ricardo Bedin Grando, Daniel Fernando Tello Gamarra,
• Abstract summary: This paper presents a novel model-based RL framework built on top of the DreamerV3 algorithm.<n>It integrates a Multi-Layer Perceptron Variational Autoencoder (MLP-VAE) within a world model to encode high-dimensional LIDAR readings into compact latent representations.<n>Experiments on simulated TurtleBot3 navigation tasks demonstrate that the proposed architecture achieves faster convergence and higher success rate.
• Score: 0.7239024032079358
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous navigation of terrestrial robots using Reinforcement Learning (RL) from LIDAR observations remains challenging due to the high dimensionality of sensor data and the sample inefficiency of model-free approaches. Conventional policy networks struggle to process full-resolution LIDAR inputs, forcing prior works to rely on simplified observations that reduce spatial awareness and navigation robustness. This paper presents a novel model-based RL framework built on top of the DreamerV3 algorithm, integrating a Multi-Layer Perceptron Variational Autoencoder (MLP-VAE) within a world model to encode high-dimensional LIDAR readings into compact latent representations. These latent features, combined with a learned dynamics predictor, enable efficient imagination-based policy optimization. Experiments on simulated TurtleBot3 navigation tasks demonstrate that the proposed architecture achieves faster convergence and higher success rate compared to model-free baselines such as SAC, DDPG, and TD3. It is worth emphasizing that the DreamerV3-based agent attains a 100% success rate across all evaluated environments when using the full dataset of the Turtlebot3 LIDAR (360 readings), while model-free methods plateaued below 85%. These findings demonstrate that integrating predictive world models with learned latent representations enables more efficient and robust navigation from high-dimensional sensory data.

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