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Self-* Storage
Greg Ganger, Carnegie Mellon University
Human administration for storage is too difficult, with industry reports indicating absurd ratios like "one human needed for each 1-10TB" and "4-7 dollars spent on managing each dollar of storage capital." This dilemma pushes us to pursue "self-*" storage infrastructures: self-organizing, self-configuring, self-tuning, self-healing, self-managing, scalable systems of storage servers. We are constructing a versatile cluster-based storage system to be deployed at CMU as a real resource for other research teams and a source of real experience for PDL researchers, as we pursue the ideal of Self-* Storage. This talk will provide a high-level overview of the project, description of the system architecture, and some early experiences with performance prediction support simplifying tuning and provisioning decisions.
Greg Ganger is a professor in the ECE department at Carnegie Mellon University. Greg is also the Director of CMU's Parallel Data Lab, academia's premier storage systems research center. Some current projects explore storage survivability, personal/home storage management, data center efficiency and automation, and of course self-* storage systems. His Ph.D. in Computer Science and Engineering is from The University of Michigan.
The Humanoid Robotics Dream
James Kuffner, The Robotics Institute, Carnegie Mellon University
http://www.kuffner.org/james/
One of the grand challenges in artificial intelligence is to create autonomous humanoid robots. The anthropomorphic shape of a humanoid should enable operation in environments designed for humans, the utilization of human tools and interfaces, and the natural use of human gestures and non-verbal communication. Fundamentally, an intelligent humanoid should be a truly "general-purpose" robot, able to accomplish any task a real human can. 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 on several humanoid platforms around the world will be shown, along with some new efforts in "mobile manipulation". Finally, the long-term prospects for the future development of robot autonomy and artificial intelligence based on planning algorithms will be discussed.
James Kuffner is an Assistant Professor at the Robotics Institute, 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 spent two years as a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo working on software and planning algorithms for humanoid robots. He joined the faculty at Carnegie Mellon University's Robotics Institute in 2002. He has published over 100 technical papers and received the Okawa Foundation Award for Young Researchers in 2007.