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Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.
Sommario
Introduction.- The Class.- Initial Behaviors.- Vision.- Movement.- Fall Detection.- Kicking.- Localization.- Communication.- General Architecture.- Global Map.- Behaviors.- Coordination.- Simulator.- UT Assist.- Conclusion.
Info autore
Dr. Peter Stone is an Alfred P. Sloan Research Fellow and Assistant Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D. in 1998 and his M.S. in 1995 from Carnegie Mellon University, both in Computer Science. He received his B.S. in Mathematics from the University of Chicago in 1993. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. Prof. Stone's research interests include planning, machine learning, multiagent systems, robotics, and e-commerce. Application domains include robot soccer, autonomous bidding agents, traffic management, and autonomic computing. His doctoral thesis research contributed a flexible multiagent team structure and multiagent machine learning techniques for teams operating in real-time noisy environments in the presence of both teammates and adversaries. He has developed teams of robot soccer agents that have won six robot soccer tournaments (RoboCup) in both simulation and with real robots. He has also developed agents that have won four auction trading agent competitions (TAC). Prof. Stone is the author of Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (MIT Press, 2000). 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. Most recently, he was awarded the prestigious IJCAI 2007 Computers and Thought award.
