Publications

Abstract

Libraries are very often considered the hallway to developing knowledge. However, the lack of adequate staff within Indian libraries makes catering to the visitors’ needs difficult. Previous systems that have sought to address libraries’ needs through automation have mostly been limited to storage and fetching aspects while lacking in their interaction aspect. We propose to address this issue by incorporating social robots within Indian libraries that can communicate and address the visitors’ queries in a multi-modal fashion attempting to make the experience more natural and appealing while helping reduce the burden on the librarians. In this paper, we propose and deploy a Furhat robot as a robot librarian by programming it on certain core librarian functionalities. We evaluate our system with a physical robot librarian (N = 26). The results show that the robot librarian was found to be very informative and overall left with a positive impression and preference.

Abstract

The increased interest in developing next-gen social robots has raised questions about the factors affecting the perception of robot emotions. This study investigates the impact of robot appearances (human-like, mechanical) and face regions (full-face, eye-region) on human perception of robot emotions. A between-subjects user study (N = 305) was conducted where participants were asked to identify the emotions being displayed in videos of robot faces, as well as a human baseline. Our findings reveal three important insights for effective social robot face design in Human-Robot Interaction (HRI): Firstly, robots equipped with a back-projected, fully animated face – regardless of whether they are more human-like or more mechanical-looking – demonstrate a capacity for emotional expression comparable to that of humans. Secondly, the recognition accuracy of emotional expressions in both humans and robots declines when only the eye region is visible. Lastly, within the constraint of only the eye region being visible, robots with more human-like features significantly enhance emotion recognition.

Abstract

Data augmentation techniques have been widely used in visual recognition tasks as it is easy to generate new
data by simple and straight forward image transformations. However, when it comes to text data augmen-
tations, it is difficult to find appropriate transformation techniques which also preserve the contextual and
grammatical structure of language texts. In this paper, we explore various text data augmentation techniques
in text space and word embedding space. We study the effect of various augmented datasets on the efficiency
of different deep learning models for relation classification in text.