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arXiv · 2409.10532

Slug Mobile: Test-Bench for RL Testing

Jonathan Wellington Morris·Vishrut Shah·Alex Besanceney·Daksh Shah·Leilani H. Gilpin
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Abstract

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.

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Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin. 2025-03-06. Slug Mobile: Test-Bench for RL Testing. https://arxiv.org/abs/2409.10532

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