Slug Mobile: Test-Bench for RL Testing

TL;DR

Slug Mobile is a scaled autonomous vehicle platform designed to bridge the sim-to-real gap in reinforcement learning for autonomous vehicles, incorporating neuromorphic hardware for advanced sensor integration.

Contribution

It introduces a scalable test-bench platform with neuromorphic sensors to improve transferability of RL models from simulation to real-world vehicles.

Findings

Developed a one-tenth scale autonomous vehicle platform.

Integrated Dynamic Vision Sensor for neuromorphic computing.

Facilitates research on sim-to-real transfer in autonomous driving.

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.