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Paplu, S. H., Mishra, C., & Berns, K. (2020). Pseudo-randomization in automating robot behaviour during human-robot interaction. In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 1-6). Institute of Electrical and Electronics Engineers. doi:10.1109/ICDL-EpiRob48136.2020.9278115.
Abstract
Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach.Files private
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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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