LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS

Abstract

Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. THE LEARNING PARADIGM The approach to learning behaviors for robots described here reflects a particular methodology for learning via a simulation model. The motivation is that making mistakes on real systems may be costly or dangerous. In addition, time constraints might limit the number of experiences during learning in the real world, while in many cases, the simulation model can be made to run faster than real time. Since learning may require experimenting with behaviors that might occasionally produce unacceptable results if applied to the real world, or might require too much time in the real environment, we assume that hypothetical behaviors will be evaluated in a simulation model (the off-line system). As illustrated in Figure 1, the current best behavior can be placed in...