Student Posters Repository

The development of a cloud-based parallel framework is proposed for analysis and interpretation of multi-media data to make intellectual interaction between socially-assistive robot and a human. In recent years, the effects of Big Data Analytics have received special attention due to their potential use in robotics. A dynamic and parallel planning of a cloud-based framework has been proposed for the collective learning by robots. Human-robot interaction requires more human-like robots that have cognitive capacities about environmental interpretation in association with objects and the surroundings. Collective robot learning is a new form of distributed and parallel computing through sharing of information and cooperation between robots. Hierarchical Deep Visual Learning Architecture is a scalable and distributed framework for visual learning using a hierarchical deep learning approach. It consists of different layers, where each layer will be a learning strategy to effectively improve the insights. The different layers are namely, Visual Feature and Signature Learning, Visual Object Inferencing, Contextual Expansion, and Communication. These layers are based on an extension of neural network models such as Convolution Neural Network, Long Short-Term Memory Network. The final layer is combinations of models from Neural Network and Semantic Web acting as the knowledge base for the robot.