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Anis Sahbani
Researcher at Centre national de la recherche scientifique
Publications - 44
Citations - 1490
Anis Sahbani is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: GRASP & Motion planning. The author has an hindex of 16, co-authored 37 publications receiving 1284 citations. Previous affiliations of Anis Sahbani include Laboratory for Analysis and Architecture of Systems & International Society for Intelligence Research.
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Journal ArticleDOI
An overview of 3D object grasp synthesis algorithms
TL;DR: This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands by focusing on analytical as well as empirical grasp synthesis approaches.
Journal ArticleDOI
Manipulation Planning with Probabilistic Roadmaps
TL;DR: A general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches.
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Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients.
Nathanaël Jarrassé,Nathanaël Jarrassé,Nathanaël Jarrassé,Tommaso Proietti,Tommaso Proietti,Tommaso Proietti,Vincent Crocher,Vincent Crocher,Vincent Crocher,Johanna Robertson,Anis Sahbani,Anis Sahbani,Anis Sahbani,Guillaume Morel,Guillaume Morel,Guillaume Morel,Agnès Roby-Brami +16 more
TL;DR: The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination in stroke patients.
Journal ArticleDOI
Constraining Upper Limb Synergies of Hemiparetic Patients Using a Robotic Exoskeleton in the Perspective of Neuro-Rehabilitation
TL;DR: An original feature of this robot controller is that the hand trajectory is not imposed on the patient: only the coordination law is modified, and results demonstrate that the desired inter-joint coordination was successfully enforced, without significantly modifying the trajectory of the end point.
Proceedings ArticleDOI
Dexterous manipulation planning using probabilistic roadmaps in continuous grasp subspaces
TL;DR: This method computes both object and finger trajectories as well as the finger relocation sequence under quasi-static movement assumption and uses a special structuring of the research space that allows to search for paths directly in the particular subspace GSn which is the subspace of all the grasps that can be achieved with n grasping fingers.