Coordination Funds

协调基金

• 批准号: 334136668
• 负责人: Professor Dr. Jürgen Gall
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/334136668?language=en
• 关键词: Coordination; Funds

In the last years, we have seen a tremendous progress in the capabilities of computer systems to classify image or video clips taken from the Internet or to analyze human pose in real-time for gaming applications. These systems, however, analyze the past or in the case of real-time systems the present with a delay of a few milliseconds. For applications, where a moving system has to react or interact with humans, this is insufficient. For instance, robots collaborating with humans need not only to perceive the current situation, but they need to anticipate human actions and the resulting future situations in order to plan their own actions.In this project, we aim to develop the technology that lays the foundation for applications that require the anticipation of human behavior. Instead of addressing the problem at a limited scope, the project addresses all relevant aspects including time horizons ranging from milliseconds to hours and granularity ranging from detailed human motion to coarse action labels. To ensure that the developed methods are not limited to a single task but can be applied for a large variety of applications, we do not solve sub-problems in isolation but address all relevant aspects jointly. The goal is therefore to develop a framework that seamlessly anticipates human behavior at all levels ranging from discrete activity labels for long-term prediction to detailed human motion for short term prediction.As a scenario for an application, we focus on human support robots that support impaired or elderly people at home. Human support robots can fill the gap that we need to face due to the demographic change that will change the population structure in Germany and other countries dramatically. However, they need the ability to anticipate human behavior at various levels of granularity in order to be accepted and be efficient. The robot needs to know when its help is needed, but it should not stand in the way. In a collaborative setting, the robot is expected to complete tasks together with a human. This requires to anticipate both the intention but also detailed movements, e.g., when jointly assembling an object or preparing a meal.

在过去的几年里，我们已经看到了计算机系统的能力，从互联网上拍摄的图像或视频剪辑分类或分析实时游戏应用程序的人体姿势的巨大进步。然而，这些系统分析过去，或者在实时系统的情况下，以几毫秒的延迟分析现在。对于移动系统必须与人类反应或交互的应用，这是不够的。例如，与人类合作的机器人不仅需要感知当前的状况，还需要预测人类的行动以及由此产生的未来状况，从而制定自己的行动计划。本项目的目的是开发为需要预测人类行为的应用奠定基础的技术。该项目没有在有限的范围内解决问题，而是解决了所有相关方面，包括从毫秒到小时的时间范围以及从详细的人体运动到粗略的动作标签的粒度。为了确保开发的方法不限于单一的任务，但可以应用于各种各样的应用程序，我们不孤立地解决子问题，但共同解决所有相关方面。因此，我们的目标是开发一个框架，无缝地预测人类行为的各个层面，从长期预测的离散活动标签到短期预测的详细人体运动。作为一个应用程序的场景，我们专注于人类支持机器人，支持家中的残疾人或老年人。人类支持机器人可以填补我们需要面对的差距，由于人口结构的变化，这将大大改变德国和其他国家的人口结构。然而，它们需要能够在各种粒度级别上预测人类行为，以便被接受和高效。机器人需要知道什么时候需要它的帮助，但它不应该碍事。在协作环境中，机器人预计将与人类一起完成任务。这需要预测意图和详细的动作，例如，当共同组装物体或准备膳食时。