自律走行の学習：人間の入力で強化されたオフロードナビゲーション
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
単眼カメラと5～10分の人間の運転デモのみを用いて、オフロード環境での走行を学習するローカルプランナを提案。手動調整なしで実世界に適応できる。
著者: Akhil Nagariya, Dimitar Filev, Srikanth Saripalli, Gaurav Pandey
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology.
