Despite advancements in socially aware navigation, robots still often behave inappropriately in social environments. To ensure successful application, robots must detect the human perceived appropriateness of their navigation behaviors. This paper presents a novel dataset covering a complete range of perceived appropriateness and uniquely incorporates human emotion and attention to facilitate the detection of perceived appropriateness of robot social navigation in pathways (PARSNiP). It is created based on a series of human-robot interaction experiments with 30 participants and a mobile robot. Several typical machine learning models are utilized to evaluate the dataset and analyze the contributions of different features in detecting perceived appropriateness. The results indicate that incorporating emotional and attentional features can significantly improve the accuracy of perceived appropriateness detection. There was an increase from 63% to 68% using algorithm-predicted emotional and attentional features, and a further increase to 79% with the emotion and attention data reported by the participants. With the dataset, researchers could train machine learning models to enable robots to detect perceived appropriateness accurately, fostering adaptations that improve their responsiveness and accuracy in social interactions. The dataset is available for download at https://github.com/duibcuiegiosahxois/PARSNiP.git, and videos will be shared upon request by contacting Y.Zhou-13@tudelft.nl.

Robots navigating in social environments inevitably exhibit behavior perceived as inappropriate by people, which they will repeat unless they are aware of them; hindering their social acceptance. This highlights the importance of robots detecting and adapting to the perceived appropriateness of their behavior, in line with what we found in a systematic literature review. Therefore, we have conducted experiments (both outdoor and indoor) to understand the perceived appropriateness of robot social navigation behavior, based on which we collected a dataset and developed a machine learning model for detecting such perceived appropriateness. To investigate the usefulness of such information and inspire robot adaptive navigation behavior design, we will further conduct aWoZ study to understand how trained human operators adapt robot behavior to people's feedback. In all, this work will enable robots to better remediate their inappropriate behavior, thus improving their social acceptance.