Sim-to real gap in Reinforcement Learning is when a model trained in a simulator does not translate to the real world. This is a problem for Autonomous Vehicles (AVs) as vehicle dynamics can vary from simulation to reality, and also from vehicle to vehicle. Slug Mobile is a one tenth scale autonomous vehicle created to help address the sim-to-real gap for AVs by acting as a test-bench to develop models that can easily scale from one vehicle to another. In addition to traditional sensors found in other one tenth scale AVs, we have also included a Dynamic Vision Sensor so we can train Spiking Neural Networks running on neuromorphic hardware.
View on arXiv@article{morris2025_2409.10532,
title={ Slug Mobile: Test-Bench for RL Testing },
author={ Jonathan Wellington Morris and Vishrut Shah and Alex Besanceney and Daksh Shah and Leilani H. Gilpin },
journal={arXiv preprint arXiv:2409.10532},
year={ 2025 }
}