论文标题
对机器人通过人群导航的机器学习方法的比较评估
A comparative evaluation of machine learning methods for robot navigation through human crowds
论文作者
论文摘要
机器人通过人群导航对AI系统构成了艰巨的挑战，因为这些方法应导致快速有效的运动，但同时不允许损害安全性。迄今为止，大多数方法都集中在探路算法与机器学习的结合上，以进行行人步行预测。最近，研究文献中提出了加强学习技术。在本文中，我们对从纽约大中央车站拍摄的监视视频收集的人群移动数据集进行了对探路/预测和强化学习方法的比较评估。结果表明，使用最先进的行为预测技术，最先进的强化学习方法与探路相关的强烈优势。
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety. Most approaches to date were focused on the combination of pathfinding algorithms with machine learning for pedestrian walking prediction. More recently, reinforcement learning techniques have been proposed in the research literature. In this paper, we perform a comparative evaluation of pathfinding/prediction and reinforcement learning approaches on a crowd movement dataset collected from surveillance videos taken at Grand Central Station in New York. The results demonstrate the strong superiority of state-of-the-art reinforcement learning approaches over pathfinding with state-of-the-art behaviour prediction techniques.
