MILER框架实现无结构化自动驾驶的仿真到现实强化学习
MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
这个研究用MILER框架解决了自动驾驶中仿真到现实迁移的难题，在真实车辆上实现了零人工干预的驾驶，挺有意思的。
MILER框架通过自定义语义中层表示（MLR）模拟器进行离线训练，在部署时使用BEVFusion处理摄像头和LiDAR数据生成与MLR模拟器一致的语义鸟瞰图表示，并采用轨迹对齐策略实现感知和控制的无监督仿真到现实迁移。在包含障碍物、急弯和离线路段的多样化测试赛道上，使用两辆不同车辆行驶了17.3公里，全程无人工干预。
MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its application to real-world autonomous driving remains scarce, particularly in unstructured environments, because of the challenges associated with sim-to-real transfer for unstructured environments. In this work, we present MILER, an end-to-end policy framework with zero-shot sim-to-real transfer. During offline training, we employ a custom semantic mid-level representation (MLR) simulator and train the policy network using reinforcement learning, with its control outputs applied directly to a bicycle model. During deployment on the real vehicle, camera and LiDAR data are processed by BEVFusion to generate a semantic bird's-eye-view representation consistent with that of the MLR simulator. The actions generated by the policy network are not applied directly to the real vehicle. Instead, we employ a trajectory-alignment strategy that enables zero-shot sim-to-real transfer of both perception and control. We extensively evaluate the proposed framework on a diverse test track comprising numerous challenges, including various obstacles, hairpin curves, velocities of up to 33.6 km/h, and off-road sections. In total, we drove 17.3 km with two different vehicles on a 3.0 km test track without human intervention, thereby demonstrating the effectiveness of our approach. Furthermore, the entire software stack runs on a Jetson AGX Orin.
