![]() ![]() However, deploying such deep learning models on drones or other robotic units is not a straightforward task, since there are significant memory and model complexity constraints. CNNs can be used to perform several robotic perception tasks such as object detection and tracking, face detection and person identification, crowd detection for ensuring flight safety on drones, emergency landing point detection, etc. Deep Convolutional Neural Networks (CNNs) are among the state-of-the-art techniques for Visual Information Analysis that can provide increased perception capabilities. Unmanned Aerial Vehicles (drones) are among the robotic units that have substantial needs for autonomous control and perception due to their increasing use in several applications like transportation, inspection, surveillance and cinematography among others. However, building deep learning algorithms for highly non-linear real-world problems such as those encountered in computer vision and robotics is non-trivial and requires substantial expertise. The significant advancements that deep learning methods have brought out for large scale image classification tasks have generated a surge of excitement in applying the techniques to other problems in computer vision and more broadly into other disciplines of computer science, such as robotics. Deep learning emerged as one of the most promising research fields in artificial intelligence. Title: "Deep Learning and Robotics: perception, control and innovations"Ībstract: This keynote speech will focus on deep learning methods and their use in robotics for increased perception, control and other innovative tasks. He is the co-author of the Artificial Intelligence and Games textbook.Īristotle University of Thessaloniki, Department of Informatics, Greece He has been the General Chair of key conferences in the area of game artificial intelligence (IEEE CIG 2010) and games research (FDG 2013). He is currently an Associate Editor of the IEEE Transactions on Games and was an Associate Editor of the IEEE Transactions on Affective Computing between 20. ![]() His research has been supported by numerous national and European grants and has appeared in Science Magazine and New Scientist among other venues. He has published over 200 journal and conference papers in the aforementioned fields and his work has been cited over 6700 times with an h-index of 44. ![]() In particular, he pursues research concepts such as user experience modeling and procedural content generation for the design of personalized interactive systems for entertainment, education, training and health. He does research at the crossroads of artificial intelligence, computational creativity, affective computing, advanced game technology, and human-computer interaction. Prior to joining the Institute of Digital Games, UoM, in 2012 he was an Associate Professor at the Center for Computer Games Research at the IT University of Copenhagen. He has received the PhD degree in Informatics from the University of Edinburgh in 2006. Yannakakis ( ) is a Professor at the University of Malta (UoM) and the Director of the Institute of Digital Games. Advanced methods for autonomous game design, procedural content generation and AI-assisted game design will be showcased via a plethora of projects running currently at the Institute of Digital Games. with (or without) usĪbstract: Can computational processes and machine-crafted artifacts be considered creative? When does this happen and who judges after all? What happens when we create together with a creative machine? Do we merely create together or can a machine truly foster our creativity as human creators? When does such co-creation foster the creativity of both humans and machines? In this talk I will address the above questions by positioning computer games as the ideal application domain for artificial intelligence for the unique features they offer. ![]() Institute of Digital Games, Calamatta Building, University of Malta, Msida ![]()
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