World Models for Autonomous Navigation of Terrestrial Robots from LIDAR Observations

TL;DR

This paper introduces a model-based reinforcement learning approach using a world model with a VAE to encode high-dimensional LIDAR data, resulting in faster, more robust terrestrial robot navigation.

Contribution

It presents a novel integration of a VAE-based world model with DreamerV3 for efficient high-dimensional LIDAR processing in robot navigation.

Findings

Achieves 100% success rate on simulated TurtleBot3 tasks.

Outperforms model-free baselines like SAC, DDPG, and TD3.

Faster convergence and higher robustness in navigation.

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.