Learning and Multiagent Reasoning for Autonomous Robots
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Over the past half-century, we have transitioned from a world with
just a handful of mainframe computers owned by large corporations, to
a world in which private individuals have multiple computers in their
homes, in their cars, in their pockets, and even on their bodies.
This transition was enabled by computer science research in multiple
areas such as systems, networking, programming languages, human
computer interaction, and artificial intelligence.
We are now in the midst of a similar transition in the area of
robotics. Today, most robots are still found in controlled,
industrial settings. However, robots are starting to emerge in the
consumer market, and we are rapidly transitioning towards a time when
private individuals will have useful robots in their homes, cars, and
For robots to operate robustly in such dynamic, uncertain
environments, we are still in need of multidisciplinary research
advances in many areas such as computer vision, tactile sensing,
compliant motion, manipulation, locomotion, high-level
decision-making, and many others. This talk will focus on two
essential capabilities for robust autonomous intelligent robots,
namely online learning from experience, and the ability to interact
with other robots and with people. Examples of theoretically grounded
research in these areas will be highlighted, as well as concrete
applications in domains including robot soccer and autonomous driving.
Dr. Peter Stone is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, Fulbright Scholar, and Professor in the Department of Computer Science at the University of Texas at Austin. He 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. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. In 2007, he was awarded the prestigious IJCAI 2007 Computers and Thought award, given once every two years to the top AI researcher under the age of 35.