  Model-based reinforcement learning (MBRL) techniques have recently yielded
promising results for real-world autonomous racing using high-dimensional
observations. MBRL agents, such as Dreamer, solve long-horizon tasks by
building a world model and planning actions by latent imagination. This
approach involves explicitly learning a model of the system dynamics and using
it to learn the optimal policy for continuous control over multiple timesteps.
As a result, MBRL agents may converge to sub-optimal policies if the world
model is inaccurate. To improve state estimation for autonomous racing, this
paper proposes a self-supervised sensor fusion technique that combines
egocentric LiDAR and RGB camera observations collected from the F1TENTH Gym.
The zero-shot performance of MBRL agents is empirically evaluated on unseen
tracks and against a dynamic obstacle. This paper illustrates that multimodal
perception improves robustness of the world model without requiring additional
training data. The resulting multimodal Dreamer agent safely avoided collisions
and won the most races compared to other tested baselines in zero-shot
head-to-head autonomous racing.
