Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloThe role of learning and kinematic features in dexterous manipulation: A comparative study with two robotic hands
Anno di pubblicazione2013
Autore/iCiancio, Anna Lisa; Zollo, Loredana; Baldassarre, Gianluca; Caligiore, Daniele; Guglielmelli, Eugenio
Affiliazioni autoriLaboratory of Biomedical Robotics and Biomicrosystems; Istituto Di Scienze E Tecnologie Della Cognizione, Rome
Autori CNR e affiliazioni
  • inglese
AbstractDexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from - or involving - cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands. © 2013 Ju et al.
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RivistaInternational journal of advanced robotic systems (Print)
Attiva dal 2004
Editore: Institute for Production Engineering. Intelligent Manufacturing Systems. Vienna University of Technology. - Wien
Paese di pubblicazione: Austria
Lingua: inglese
ISSN: 1729-8806
Titolo chiave: International journal of advanced robotic systems (Print)
Titolo abbreviato: Int. j. adv. robot. syst. (Print)
Titoli alternativi:
  • ARS (Print)
  • ARS journal (Print)
Numero volume della rivista10
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Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84885572903)
Parole chiaveBio-inspired control, Central pattern generator, Hierarchical neural network, Reinforcement learning, Robotic manipulation
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Strutture CNR
  • ISTC — Istituto di scienze e tecnologie della cognizione
Moduli/Attività/Sottoprogetti CNR-
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