Using Hierarchical Dynamical Systems to Control Reactive Behavior
RoboCup-99: Robot Soccer World Cup III
Reinforcement Learning for 3 vs. 2 Keepaway
RoboCup 2000: Robot Soccer World Cup IV
Robust Real Time Color Tracking
RoboCup 2000: Robot Soccer World Cup IV
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Local Movement Control with Neural Networks in the Small Size League
RoboCup 2006: Robot Soccer World Cup X
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We show how to apply learning methods to two robotics problems, namely the optimization of the on-board controller of an omnidirectional robot, and the derivation of a model of the physical driving behavior for use in a simulator. We show that optimal control parameters for several PID controllers can be learned adaptively by driving an omni directional robot on a field while evaluating its behavior, using an reinforcement learning algorithm. After training, the robots can follow the desired path faster and more elegantly than with manually adjusted parameters. Secondly, we show how to learn the physical behavior of a robot. Our system learns to predict the position of the robots in the future according to their reactions to sent commands. We use the learned behavior in the simulation of the robots instead of adjusting the physical simulation model whenever the mechanics of the robot changes. The updated simulation reflects then the modified physics of the robot.