シミュレーションと実機のギャップを考慮したマイクロモビリティ向けエンドツーエンド学習環境
Sim-to-Real Aware End-to-End Learning Environment for Micromobility
WHILL Model CRを対象に、ベイズ最適化で物理パラメータを調整してsim-to-realギャップを低減し、DreamerV3で学習した方策を実機に直接転移させた研究。
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著者: Shouma Amano, Takuya Azumi
分類: cs.RO
原文アブストラクト
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.