A learning and control approach based on the human neuromotor system

Current models of human motor learning and control typically employ continuous (or near continuous) movement commands and sensory information. However, research suggests that voluntary motor commands are issued in discrete-time submovements. There is also reasonable support for the hypothesis that human sensory experience is episodic as well. These facts have motivated the development of a learning algorithm that employs discrete-time sensory and motor control events, S-learning. We present this algorithm together with the results of simulated robot control. The results show that the learning that takes place is adaptive and is robust to a variety of conditions that many traditional controllers are not capable of handling, including random errors in the actuators and sensors, random transmission time delays, hard nonlinearities, time varying system behavior, and unknown structure of system dynamics. The performance of S-learning suggests that it may be an appropriate high-level control scheme for complex robotic systems, including walking, cooperative manipulation, and humanoid robots