Robot-Centric Perception of Human Groups
Robot-Centric Perception of Human Groups
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DOI：
10.1145/3375798
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Angelique Taylor;Darren M. Chan;L. Riek
中科院分区：
文献类型：
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作者：
Angelique Taylor;Darren M. Chan;L. Riek
机器人社区不断努力创造可在现实环境中部署的机器人。通常，人们期望机器人与人类群体互动。为了实现这一目标，我们引入了一种新的方法，即以机器人为中心的群体估计模型（RoboGEM），它使机器人能够检测人群。文献中报道的大部分工作都集中在二元相互作用上，这在我们对如何制造能够与更大群体有效合作的机器人的理解上留下了空白。此外，目前的许多方法依赖于外心视觉，将摄像头和传感器放置在外部环境中，而不是安装在机器人身上。因此，这些方法对于非结构化、以人为中心的环境中的机器人来说是不切实际的，因为这些环境是新颖的、不可预测的。此外，大多数关于群体感知的工作都是有监督的，这可能会抑制现实环境中的表现。RoboGEM通过使用颜色和深度（RGB-D）数据从自我中心的角度来预测社会群体，从而解决了这些差距。为了实现群体预测，RoboGEM利用关节运动和接近估计。我们针对一个具有挑战性的、以自我为中心的真实世界数据集对RoboGEM进行了评估，其中行人和机器人同时处于运动状态，结果显示RoboGEM在检测精度方面优于两种最先进的监督方法，准确率高达30%，漏检率更低。我们的工作将对机器人社区有所帮助，并作为构建无监督系统的里程碑，使机器人能够在现实环境中与人类群体合作。
The robotics community continually strives to create robots that are deployable in real-world environments. Often, robots are expected to interact with human groups. To achieve this goal, we introduce a new method, the Robot-Centric Group Estimation Model (RoboGEM), which enables robots to detect groups of people. Much of the work reported in the literature focuses on dyadic interactions, leaving a gap in our understanding of how to build robots that can effectively team with larger groups of people. Moreover, many current methods rely on exocentric vision, where cameras and sensors are placed externally in the environment, rather than onboard the robot. Consequently, these methods are impractical for robots in unstructured, human-centric environments, which are novel and unpredictable. Furthermore, the majority of work on group perception is supervised, which can inhibit performance in real-world settings. RoboGEM addresses these gaps by being able to predict social groups solely from an egocentric perspective using color and depth (RGB-D) data. To achieve group predictions, RoboGEM leverages joint motion and proximity estimations. We evaluated RoboGEM against a challenging, egocentric, real-world dataset where both pedestrians and the robot are in motion simultaneously, and show RoboGEM outperformed two state-of-the-art supervised methods in detection accuracy by up to 30%, with a lower miss rate. Our work will be helpful to the robotics community, and serve as a milestone to building unsupervised systems that will enable robots to work with human groups in real-world environments.