Slug Mobile：強化学習テスト用の1/10スケール自律走行車両
Slug Mobile: Test-Bench for RL Testing
シミュレーションと実機のギャップを埋めるため、車両間でスケールしやすいモデル開発を目的とした1/10スケールの自律走行車両を構築し、動的視覚センサも搭載した。
著者: Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin
分類: cs.RO, cs.LG
原文アブストラクト
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.