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Antonio Morales
Researcher at James I University
Publications - 64
Citations - 2412
Antonio Morales is an academic researcher from James I University. The author has contributed to research in topics: GRASP & Robot. The author has an hindex of 21, co-authored 61 publications receiving 1983 citations.
Papers
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Journal ArticleDOI
Data-Driven Grasp Synthesis—A Survey
TL;DR: A review of the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps and an overview of the different methodologies are provided, which draw a parallel to the classical approaches that rely on analytic formulations.
Journal ArticleDOI
Data-Driven Grasp Synthesis - A Survey
TL;DR: A survey of data-driven grasp synthesis can be found in this article, where the authors divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects.
Book ChapterDOI
OpenGRASP: a toolkit for robot grasping simulation
Beatriz León,Stefan Ulbrich,Rosen Diankov,Gustavo Puche,Markus Przybylski,Antonio Morales,Tamim Asfour,Sami Moisio,Jeannette Bohg,James J. Kuffner,Rüdiger Dillmann +10 more
TL;DR: A new simulation toolkit for grasping and dexterous manipulation called OpenGRASP addressing those aspects in addition to extensibility, interoperability and public availability, based on a modular architecture that supports the creation of new functionality and the integration of existing and widely-used technologies and standards.
Proceedings ArticleDOI
Mind the gap - robotic grasping under incomplete observation
Jeannette Bohg,Matthew Johnson-Roberson,Beatriz León,Javier Felip,Xavi Gratal,Niklas Bergström,Danica Kragic,Antonio Morales +7 more
TL;DR: The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world by providing a completed state estimate of the environment to a simulator in which stable grasps and collision-free movements are planned.
Proceedings ArticleDOI
Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands
TL;DR: A framework for grasp planning with a humanoid robot arm and a five-fingered hand is presented, based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace.