As mobile robots start operating in environments crowded with humans, human-aware navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear route leading to it. There are significant efforts to solve this problem for mobile robots; however, they are not scalable to high human density and learning based approaches depend heavily on the context and configuration of the set they are trained with. We develop a method which infers initial trajectories from Gaussian processes and updates these trajectories jointly for all agents using a cost based interaction approach. We condition Gaussian processes online with the best hypothesis at each step of prediction horizon. The method is tested on a common public dataset and it is shown that it outperforms two state-of-the-art approaches in terms of human-likeness of predicted trajectories.

随着移动机器人开始在人满为患的环境中运行，需要感知人类的导航才能使这些机器人安全，高效和符合社会要求地导航。人们以交互合作的方式导航，因此即使没有通向目的地的清晰路线，他们也可以找到到达目的地的路径。为了解决移动机器人的这一问题，人们付出了巨大的努力。但是，它们不能扩展到高密度的人员，并且基于学习的方法在很大程度上取决于训练他们的场景的背景和配置。我们开发了一种从高斯过程中推断初始轨迹并使用基于成本的交互方法为所有代理共同更新这些轨迹的方法。我们在预测范围的每一步以最佳假设在线调节高斯过程。该方法在一个公共公共数据集上进行了测试，结果表明，就预测轨迹的人性而言，该方法优于两种最新方法。