论文标题
Navigan：一种具有社会符合社会符合性导航的生成方法
NaviGAN: A Generative Approach for Socially Compliant Navigation
论文作者
论文摘要
在人类人群中导航的机器人不仅需要针对其任务绩效，而且要遵守社会规范来优化其道路。在这种情况下，主要挑战之一是缺乏评估和优化社会符合社会符合性行为的标准指标。社会导航中的现有作品可以根据其优化目标的差异进行分组。例如，强化学习方法倾向于对\ textit {comfort}的方面进行优化，而逆强化学习方法旨在实现\ textit {自然}行为。在本文中，我们提出了Navigan，这是一种生成导航算法，共同优化了\ textit {comfort}和\ textit {naturalness}方面。我们的方法被设计为一种对抗性训练框架，可以学会生成一条导航路径，该导航路径既适合实现目标又遵守潜在的社会规则。在多个数据集上进行了一组实验，以定量证明所提出的方法的优势。我们还使用实际机器人在现实世界环境中进行了广泛的实验，以定性地评估训练有素的社会导航行为。机器人实验的视频记录可以在链接中找到：https：//youtu.be/61bldymjcpw。
Robots navigating in human crowds need to optimize their paths not only for their task performance but also for their compliance to social norms. One of the key challenges in this context is the lack of standard metrics for evaluating and optimizing a socially compliant behavior. Existing works in social navigation can be grouped according to the differences in their optimization objectives. For instance, the reinforcement learning approaches tend to optimize on the \textit{comfort} aspect of the socially compliant navigation, whereas the inverse reinforcement learning approaches are designed to achieve \textit{natural} behavior. In this paper, we propose NaviGAN, a generative navigation algorithm that jointly optimizes both of the \textit{comfort} and \textit{naturalness} aspects. Our approach is designed as an adversarial training framework that can learn to generate a navigation path that is both optimized for achieving a goal and for complying with latent social rules. A set of experiments has been carried out on multiple datasets to demonstrate the strengths of the proposed approach quantitatively. We also perform extensive experiments using a physical robot in a real-world environment to qualitatively evaluate the trained social navigation behavior. The video recordings of the robot experiments can be found in the link: https://youtu.be/61blDymjCpw.
