Adaptive visual pursuit involving eye-head coordination and prediction of the target motion

Nowadays, increasingly complex robots are being designed. As the complexity of robots increases, traditional methods for robotic control fail, as the problem of finding the appropriate kinematic functions can easily become intractable. For this reason the use of neuro-controllers, controllers based on machine learning methods, has risen at a rapid pace. This kind of controllers are especially useful in the field of humanoid robotics, where it is common for the robot to perform hard tasks in a complex environment. A basic task for a humanoid robot is to visually pursue a target using eye-head coordination. In this work we present an adaptive model based on a neuro-controller for visual pursuit. This model allows the robot to follow a moving target with no delay (zero phase lag) using a predictor of the target motion. The results show that the new controller can reach a target posed at a starting distance of 1.2 meters in less than 100 control steps (1 second) and it can follow a moving target at low to medium frequencies (0.3 to 0.5 Hz) with zero-lag and small position error (less then 4 cm along the main motion axis). The controller also has adaptive capabilities, being able to reach and follow a target even when some joints of the robot are clamped.