Simulation Based Selection of Actions for a Humanoid Soccer-Robot

This paper introduces a method for making fast decisions in a highly dynamic situation, based on forward simulation. This approach is inspired by the decision problem within the RoboCup domain. In this environment, selecting the right action is often a challenging task. The outcome of a particular action may depend on a wide variety of environmental factors, such as the robot’s position on the field or the location of obstacles. In addition, the perception is often heterogeneous, uncertain, and incomplete. In this context, we investigate forward simulation as a versatile and extensible yet simple mechanism for inference of decisions. The outcome of each possible action is simulated based on the estimated state of the situation. The simulation of a single action is split into a number of simple deterministic simulations – samples – based on the uncertainties of the estimated state and of the action model. Each of the samples is then evaluated separately, and the evaluations are combined and compared with those of other actions to inform the overall decision. This allows us to effectively combine heterogeneous perceptual data, calculate a stable decision, and reason about its uncertainty. This approach is implemented for the kick selection task in the RoboCup SPL environment and is actively used in competitions. We present analysis of real game data showing significant improvement over our previous methods.