A Brain-Inspired Model of Reaching and Adaptation on the iCub Robot

Motor learning is an important property for em-bodied agents and a wide variety of approaches has been developed for humanoid robots. One strategy is to study the human brain and replicate the structure and functionality of the brain in neuro-computational models. These models are built at different levels of abstraction to perform a variety of tasks. The presented work integrates a recent system-level neuro-computational model incorporating the motor cortex basal ganglia loops, the cerebellum and central pattern generators with the iCub robot. The iCub is a humanoid robot explicitly developed for cognitive research and well suited for manipulation and other motor tasks due to its high degrees of freedom. To test the performance of the model two experiments are conducted. In the first step a reaching task is carried out. Here, the task is to perform movements from a fixed initial position to different goal locations in the 3D-space. The results are compared to the prior performance of the model to verify the model and code adjustments and the learning abilities. In the second setup a simple motor adaptation task is realized, where an external shift perturbation is introduced to the arm motion, and the adaptation abilities of the model are investigated.