User observation & dataset collection for robot training

Abstract

Personal robots operate in human environments such as homes and offices, co-habiting with people. To effectively train robot algorithms for such scenarios, a large amount of training data containing both people and the environment is required. Collecting such data involves taking a robot into new environments, observing and interacting with people. So far, best practices for robot data collection have been undefined. Fortunately, the human-robot interaction community has conducted field studies whose methodology can serve as a model. In this paper, we draw parallels between field study observation and the data collection process, suggesting that best practices may be transferable. As a use case, we present a robot sensor dataset for training and testing algorithms for person detection in indoor environments.