The human hand is considered as the highest example of dexterous system capable of interacting with different objects and adapting its manipulation abilities to them. The control of poliarticulated prosthetic hands represents one important research challenge, typically aiming at replicating the manipulation capabilities of the natural hand. For this reason, this paper wants to propose a bio-inspired learning architecture based on parallel force/position control for prosthetic hands, capable of learning cyclic manipulation capabilities. To this purpose, it is focused on the control of a commercial biomechatronic hand (the IH2 hand) including the main features of recent poliarticulated prosthetic hands. The training phase of the hand was carried out in simulation, the parallel force/position control was tested in simulation whereas preliminary tests were performed on the real IH2 hand. The results obtained in simulation and on the real hand provide an important evidence of the applicability of the bio-inspired neural control to real biomechatronic hand with the typical features of a hand prosthesis.
Hand Humans