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Christopher M. Brown

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2 papers

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IJCAI Conference 1995 Conference Paper

Adaptable Planner Primitives for Real-World Robotic Applications

Robert W. Wisniewski
Christopher M. Brown

With increased processor speed and improved robotic and AI technology, researchers are be ginning to design programs that can behave in telligently and interact in the real world. A large increase in processing power has come from parallel machines, but taking advantage of this power is challenging. In this paper we address the issues in designing planners for real-time AI and robotic applications, and provide guiding principles. These principles were designed to minimize the difference be tween the new real-time model and the stan dard off-line model. Applying these princi ples yields a better-structured application, eas ier design and implementation, and improved performance. The focus of the paper is on a design methodology for implementing effec tive planners in real-world applications. Using Ephor (our runtime environment), and apply ing the described planner principles, we demon strate improved performance in a real-world shepherding application.

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IROS Conference 1995 Conference Paper

Cooperative coaching in robot learning

Jeff G. Schneider
Christopher M. Brown

Many closed loop learning algorithms perform gradient descent on a cost function with respect to the parameters of a learning controller. The authors observe that both local closed loop learners, which consider only the cost of the current time step, and optimal control based closed loop learners, which consider the future effects of control actions, can become stuck in sub-optimal local minima in the cost function. The authors propose the use of "cooperating coaches" to deal with this problem. Each coach attempts gradient descent based on its own cost function and they work together to avoid getting stuck in local minima. When one coach has achieved the best result it can (the gradient for its cost function is zero), another coach takes over to guide the search through the parameter space. The authors demonstrate cooperative coaching on the problem of curve tracking with an inverted pendulum and show that it yields faster, smoother tracking of target curves by combining the best aspects of two different coaches.

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