用1/10比例自动驾驶车构建测试平台，缩小仿真到现实的差距。
Slug Mobile: Test-Bench for RL Testing
- 搭建1/10比例自动驾驶车平台，支持多车模型迁移。
- 集成动态视觉传感器，可训练脉冲神经网络。
- 适合研究仿真实现与真实部署差距的团队。
强化学习中的仿真到现实差距指在模拟环境中训练的模型无法有效迁移到真实世界。这一问题在自动驾驶车辆（AVs）中尤为突出，因为车辆动力学在仿真与现实之间存在差异，且不同车辆间也存在差异。为缓解该问题，本文提出Slug Mobile——一种1/10比例的自动驾驶车辆，作为测试平台，用于开发可跨车辆快速迁移的模型。除传统传感器外，还集成动态视觉传感器（Dynamic Vision Sensor），以支持在类脑硬件上运行脉冲神经网络（Spiking Neural Networks）的训练。该平台旨在推动高鲁棒性、可泛化的自主系统研发。
原文摘要 · Abstract (English)
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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