Learning Flexible Motor Control

Learning Flexible Decision Making and Motor Control in Robots for Human-Robot Interaction

In our research proposal we integrate innovative concepts of systems level computational neuroscience with robotics. We will develop a neuro-computational model of cortex-basal ganglia interactions for decision making and motor control combined with two models of the cerebellum, one for open-loop control and one for the predictions of the sensory consequences of motor actions. We will test the models in simulation and on the humanoid iCub robot with artificial skin on reaching and grasping tasks where movements shall be executed towards particular goals. In collaborative work, we will extend this approach towards human-robot interaction where an object will be passed from a human to the robot.