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Ph.D. Computer Science, University of Utah 1988 M.S. Mechanical Engineering, Pennsylvania State University 1984 B.A. Physics, Franklin and Marshall College 1980 B.S. Mechanical Engineering, Washington University 1980
1995 - present: Associate Professor, Department of Computer Science, University of Massachusetts, Amherst, Massachusetts. 1988 - 95 Assistant Professor, Department of Computer Science, University of Massachusetts, Amherst, Massachusetts. 1988 - present Co-Director of the Laboratory for Perceptual Robotics, Department of Computer Science, University of Massachusetts, Amherst, Massachusetts. 1984 - 88: Research and Teaching Assistant, Departments of Computer Science and Mechanical Engineering, University of Utah, Salt Lake City, Utah. 1981 - 82: Systems Design Engineer, General Electric Company Edison Engineering Program, Louisville, Kentucky. 1980: Washington University Technical Associates, St. Louis, Missouri.
My research focus is the integration of Artificial Intelligence and Control Theory for autonomous robot control. I have been investigating the use of sensor-rich component automation as a means of enhancing the flexibility of robots applied to a diverse range of tasks. A central focus of this work is computationally tractible controllers for complex, highly redundant systems. The approach entails on-line composition of a control basis for manipulation tasks. Constraints on the expression and composition of control have been established which preserve stability and convergence characteristics in the integrated system. We have applied this paradigm to a 20 DOF hand/arm system, to a homebuilt legged platform and to a non-holonomic mobile platform as well. Also underway is a research program to characterize and exploit the underlying predictability of these control architectures to facilitate process scheduling. This work is focussed on evaluating process control alternatives with respect to quality, timeliness, and throughput in real-time, flexible automation. In the future, this work will be applied to autonomous robot control, to ``agile'' low volume manufacturing systems, and to control for intelligent vehicles and highway systems.
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2022 IEEE International Conference on Development and Learning (ICDL)pp.75-80, (2022)
Springer Proceedings in Advanced Robotics (2018): 127-143
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