title: A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints.
abstract: The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.
authors.affiliation: Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD, USA. Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA. Maryland Robotics Center, University of Maryland, College Park, MD, USA.
authors.forename: Rodolphe J
authors.initials: RJ
authors.lastname: Gentili
authors.forename: Hyuk
authors.initials: H
authors.lastname: Oh
authors.forename: Alissa V
authors.initials: AV
authors.lastname: Kregling
authors.forename: James A
authors.initials: JA
authors.lastname: Reggia
mesh_terms: D000818:Animals; D001696:Biomechanical Phenomena; D032701:Biomimetics / Q000295:instrumentation* / Q000379:methods; D003198:Computer Simulation; D017076:Computer-Aided Design; D004867:Equipment Design; D019544:Equipment Failure Analysis; D025461:Feedback, Physiological / Q000502:physiology; D005384:Finger Joint / Q000294:innervation / Q000502:physiology*; D005385:Fingers / Q000294:innervation / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D012371:Robotics / Q000295:instrumentation* / Q000379:methods