ABSTRACT:
This talk will discuss the challenge of motion autonomy for humanoid robots and present an overview of several autonomous motion generation methods designed for application tasks involving navigation, object grasping and manipulation, footstep placement, and full-body motions. Experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware will be shown. Finally, the long-term prospects for the future development of robot autonomy and artificial intelligence based on planning algorithms will be discussed.
BIOGRAPHY:
James Kuffner is an Assistant Professor at the Robotics Institute, Computer Science Dept., Carnegie Mellon University. He received a B.S. and M.S. in Computer Science from Stanford University in 1993 and 1995, and a Ph.D. from the Stanford University Dept. of Computer Science Robotics Laboratory in 1999. He was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo from 1999 to 2001. He joined the faculty at Carnegie Mellon University in May 2002. His research interests include robotics, motion planning, and computer graphics and animation. See http://www.kuffner.org/james/ for more information.
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