9421532 Nelson This award is to support the research effort for the University of Rochester's Computer Science and Mechanical Engineering Departments to build and control a pair of small, off-road vehicles; one human-piloted, and one computer-controlled. Intrinsically interesting aspects of the equipment are low cost, off-road capability, and relatively high speeds (50 kph). The two vehicles will support research on semi-autonomous control, real-time visual processing, real-time decision-making, control learning, and simulation. The research issues center around semi-autonomous or "deictic" control in which visual or symbolic commands are passed to the computer-controlled vehicle for local interpretation. This control style is increasingly popular for difficult, real-time control tasks (e.g. telemanipulation) in which neither full autonomy, nor direct teleoperation is feasible. Rochester will continue working on this topic. Deictic control does not solve all control problems: control learning can play an important role. Two aspects of control learning will be investigated: learning tunable "motor skills" necessary to cope with high-speed situations, and, at a higher level, learning sequences of appropriate actions. Both types of learning are already under investigation at Rochester. Today's learning algorithms often receive initial training in simulation, so sophisticated simulation work already underway will be used. ***
