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Mishra, C., & Skantze, G. (2022). Knowing where to look: A planning-based architecture to automate the gaze behavior of social robots. In Proceedings of the 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 1201-1208). doi:10.1109/RO-MAN53752.2022.9900740.
Abstract
Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit humanlike gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy. -
Badimala, P., Mishra, C., Venkataramana, R. K. M., Bukhari, S. S., & Dengel, A. (2019). A Study of Various Text Augmentation Techniques for Relation Classification in Free Text. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (pp. 360-367). Setúbal, Portugal: SciTePress Digital Library. doi:10.5220/0007311003600367.
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.
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