面向微移动的仿真到现实感知的端到端学习环境

中文摘要 AI 辅助

尽管端到端自动驾驶系统展现出前景，但其在微移动车辆上的应用受到模拟器无法捕捉特定运动学（如差速驱动和全向轮）的阻碍。本文提出了一种面向仿真到现实感知的、针对特定车辆的端到端学习环境，用于AWSIM和ROS 2上的WHILL Model CR。为最小化仿真到现实的差距，利用真实世界数据通过贝叶斯优化对物理参数进行优化，减少了各种驾驶场景下的轨迹误差。此外，本研究引入了一种为基于世界模型的智能体稳定训练而量身定制的同步架构。使用DreamerV3训练的端到端策略在模拟避障环境中展现出学习进展并实现了任务完成。此外，该策略展示了直接仿真到现实迁移到物理车辆的能力，使车辆能够在真实世界走廊复制环境中绕过纸箱而无需微调。本文为仿真到现实的微移动策略研究提供了实用基础。

英文摘要

While end-to-end autonomous driving systems show promise, their application to micromobility vehicles is hindered by simulators failing to capture specific kinematics, such as differential drives and omni-wheels. This paper pro- poses a sim-to-real-aware, vehicle-specific end-to-end learning environment for the WHILL Model CR on AWSIM and ROS 2. To minimize the sim-to-real gap, physical parameters are optimized via Bayesian optimization using real-world data, reducing trajectory errors across various driving scenarios. Additionally, this study introduces a synchronized architecture tailored for the stable training of world model-based agents. An end-to-end policy trained with DreamerV3 exhibited learning progress and achieved task completion in a simulated obstacle avoidance setting. Furthermore, this policy demonstrated direct sim-to-real transfer to the physical vehicle, enabling the vehicle to navigate around a cardboard box in a real-world corridor replica without fine-tuning. This paper provides a practical foundation for sim-to-real micromobility policy studies.

发表机构

• Saitama University（埼玉大学）

机构由 AI 辅助整理，请以论文原文为准。