Learning Robot Control

Learning robot control, a subclass of the field of learning control, refers to the process of acquiring a sensory-motor control strategy for a particular movement task and movement system by trial and error. Learning control is usually distinguished from adaptive control (see ADAPTIVE CONTROL) in that the learning system is permitted to fail during the process of learning, while adaptive control emphasizes single trial convergence without failure. Thus, learning control resembles the way that humans and animals acquire new movement strategies, while adaptive control is a special case of learning control that fulfills stringent performance constraints, e.g., as needed in life-critical systems like airplanes and industrial robots.