Visualitza UJI: Investigación per autoria "92b8c65e-06b9-48e6-b536-0754cb4a5907"

Ara mostrant els elements 1-3 d 3

    • openAccess   Data-Driven Grasp Synthesis—A Survey 

      Bohg, J.; Morales, Antonio; Asfour, Tamim; Kragic, D. Institute of Electrical and Electronics Engineers (IEEE) (2014-04)
      We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or ...

    • openAccess   Deep Learning Approaches to Grasp Synthesis: A Review 

      Newbury, Rhys; Gu, Morris; Chumbley, Lachlan; Mousavian, Arsalan; Eppner, Clemens; Leitner, Jürgen; Bohg, Jeannette; Morales, Antonio; Asfour, Tamim; Kragic, Danica; Fox, Dieter; Cosgun, Akansel IEEE (2023-06-13)
      Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, ...

    • openAccess   The Anthropomorphic Hand Assessment Protocol (AHAP) 

      Llop-Harillo, Immaculada; Pérez-González, Antonio; Starke, Julia; Asfour, Tamim Elsevier (2019-11)
      The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking ...