title: Incremental learning control of the DLR-HIT-Hand II during interaction tasks.
abstract: In this paper a bio-inspired control architecture for a robotic hand is presented. It relies on the same mechanisms of learning inverse internal models studied in humans. The control is capable of developing an internal representation of the hand interacting with the environment and updating it by means of the interaction forces that arise during contact. The learning paradigm exploits LWPR networks, which allow efficient incremental online learning through the use of spatially localized linear regression models. Additionally this paradigm limits negative interference when learning multiple tasks. The architecture is validated on a simulated finger of the DLR-HIT-Hand II performing closing movements in presence of two different viscous force fields, perturbing its motion.
authors.affiliation: Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, via Alvaro del Portillo 21, 00128 Roma, Italy.
authors.forename: Alessio
authors.initials: A
authors.lastname: Alessi
authors.forename: Loredana
authors.initials: L
authors.lastname: Zollo
authors.forename: Luca
authors.initials: L
authors.lastname: Lonini
authors.forename: Rosanna
authors.initials: R
authors.lastname: De Falco
authors.forename: Eugenio
authors.initials: E
authors.lastname: Guglielmelli
mesh_terms: D001185:Artificial Intelligence; D040761:Biomimetic Materials; D003198:Computer Simulation; D005246:Feedback; D006225:Hand / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D009068:Movement; D012371:Robotics / Q000295:instrumentation* / Q000379:methods*