论文标题
从有条件的神经过程的示威中学习社会导航
Learning Social Navigation from Demonstrations with Conditional Neural Processes
论文作者
论文摘要
社交能力对于现代机器人增加其在人类环境中的可接受性至关重要。传统技术使用手动工程的实用功能，灵感来自观察行人行为以实现社会导航的启发。但是，导航的社会方面是多种多样的，在不同类型的环境，社会和人口密度之间发生了变化，因此在每个域中使用手工制作的技术是不现实的。本文介绍了一种数据驱动的导航体系结构，该导航体系结构使用最先进的神经体系结构，即有条件的神经过程，以从观测值中学习移动机器人的全球和本地控制器。此外，我们利用一种最先进的深层预测机制来检测与受过训练的情况不同的情况，在这种情况下，反应控制器介入以确保安全导航。我们的结果表明，所提出的框架可以成功执行有关数据中社会规范的导航任务。此外，我们表明我们的系统会产生较少的个人区域违规行为，从而减少不适感。
Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.
