学习自主性：由人类输入增强的越野导航
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
AI总结：
本文提出一种基于学习的局部规划器，仅用单目相机和5至10分钟人类演示即可学习越野驾驶偏好，减少真实数据需求并免微调地适应多种地形。
摘要：
在自动驾驶领域，穿越越野地形面临一系列独特挑战，包括草地和泥土等不可预测的路面，以及灌木丛和水坑等突发障碍物。在本研究中，我们提出了一种新颖的基于学习的局部规划器，通过仅使用单目相机从真实世界演示中直接捕捉人类驾驶的细微差别，来应对这些挑战。该规划器的关键特性是能够在具有多种地形类型的高挑战性越野环境中导航，并具备快速学习能力。通过利用极少的人类演示数据（5至10分钟），它能快速学会在各种越野条件下行驶。该局部规划器显著减少了学习人类驾驶偏好所需的真实世界数据。这使规划器能够将学到的行为应用于真实场景，而无需手动微调，展示了越野自动驾驶技术中的快速调整与适应能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
