Vision-Based Robot Learning for Behavior Acquisition

We introduce our approach, a new direction of robotics research that makes a robot learn to behave adequately to accomplish a given task at hand through the interactions with its environment with less a priori knowledge about the environment or the robot itself. We brie y present three research topics of vision-based robot learning in each of which visual perception is tightly coupled with actuator e ects so as to learn an adequate behavior. First, \motion sketch" for a oneeyed mobile robot to learn several behaviors such as obstacle avoidance and target pursuit is introduced. Next, a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal is presented. Finally, we show a method of purposive visual control consisting of an on-line estimator and a feedback/feedforward controller for uncalibrated camera-manipulator systems. All topics include the real robot experiments.