Peter Stone, University of Texas, Austin, USA.

https://www.cs.utexas.edu/people/faculty-researchers/peter-stone



Title 
"Efficient Robot Skill Learning:  Grounded Simulation Learning and Imitation Learning from Observation"

Summary
For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and imitation learning from observation opens the possibility of robots learning from the vast trove of videos available online.

Short Bio
Dr. Peter Stone is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Director of Texas Robotics, at the University of Texas at Austin. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, and robotics. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as Executive Director of Sony AI America.

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Miguel Angel Sotelo, University of Alcalá, Spain.

http://www.isislab.es/sotelo/

Title
"Advanced Motion Prediction for Self-Driving Cars"
Self-driving cars have experienced a booming development in the latest years, having achieved a certain degree of maturity. Their scene recognition capabilities have improved in an impressive manner, especially thanks to the development of Deep Learning techniques and the availability of immense amount of data contained in well-organized public datasets. But still, self-driving cars exhibit limited ability to deal with certain types of situations that do not pose a great challenge to human drivers, such as entering a congested round-about, dealing with cyclists, or giving way to a vehicle that is aggressively merging onto the highway from a ramp lane. All these tasks require the development of advanced prediction capabilities in order to provide the most likely trajectories for all traffic agents around the ego-car, namely vehicles and vulnerable road users, in a given time horizon. This talk will present some innovative solutions for efficient motion prediction in the context of autonomous driving.

Bio-sketch
Miguel Ángel Sotelo received the degree in Electrical Engineering in 1996 from the Technical University of Madrid, the Ph.D. degree in Electrical Engineering in 2001 from the University of Alcalá (Alcalá de Henares, Madrid), Spain, and the Master in Business Administration (MBA) from the European Business School in 2008. From 1993 to 1994, he held an Excellence Research Grant at the University of Alcalá, where he is currently a Full Professor at the Department of Computer Engineering and Vice-president for International Relations. In 1997, he was a Research Visitor at the RSISE of the Australian National University in Canberra. His research interests include Self-driving cars, Cooperative Systems, and Traffic Technologies. He is author of more than 200 publications in journals, conferences, and book chapters. He has been recipient of the Best Research Award in the domain of Automotive and Vehicle Applications in Spain in 2002 and 2009, and the 3M Foundation Awards in the category of eSafety in 2004 and 2009. Miguel Ángel Sotelo has served as Project Evaluator, Rapporteur, and Reviewer for the European Commission in the field of ICT for Intelligent Vehicles and Cooperative Systems in FP6 and FP7. He was Director General of Guadalab Science & Technology Park (2011-2012) and co-founder and CEO of Vision Safety Technologies (2009-2015), a spin-off company established in 2009 to commercialize computer vision systems for road infrastructure inspection. He is member of the IEEE ITSS Board of Governors and Executive Committee. Miguel Ángel Sotelo served as Editor-in-Chief of the Intelligent Transportation Systems Society Newsletter (2013), Editor-in-Chief of the IEEE Intelligent Transportation Systems Magazine (2014-2016), Associate Editor of IEEE Transactions on Intelligent Transportation Systems (2008-2014), member of the Steering Committee of the IEEE Transactions on Intelligent Vehicles (since 2015), and a member of the Editorial Board of The Open Transportation Journal (2006-2015). He has served as General Chair of the 2012 IEEE Intelligent Vehicles Symposium (IV’2012) that was held in Alcalá de Henares (Spain) in June 2012. He was recipient of the 2010 Outstanding Editorial Service Award for the IEEE Transactions on Intelligent Transportation Systems, the IEEE ITSS Outstanding Application Award in 2013, and the Prize to the Best Team with Full Automation in GCDC 2016. At present, he is Past-President of the IEEE Intelligent Transportation Systems Society.