R
Renaud Detry
Researcher at California Institute of Technology
Publications - 68
Citations - 1541
Renaud Detry is an academic researcher from California Institute of Technology. The author has contributed to research in topics: GRASP & Pose. The author has an hindex of 21, co-authored 64 publications receiving 1352 citations. Previous affiliations of Renaud Detry include University of Liège & Université catholique de Louvain.
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Journal ArticleDOI
Combining active learning and reactive control for robot grasping
TL;DR: A hierarchical controller is proposed that is able to quickly learn good grasps of a novel object in an unstructured environment, by executing smooth reaching motions and preshaping the hand depending on the object's geometry.
Journal ArticleDOI
One-shot learning and generation of dexterous grasps for novel objects
TL;DR: A method for one-shot learning of dexterous grasps and grasp generation for novel objects using an incomplete point cloud from a depth camera and a product of experts, in which experts are of two types.
Proceedings ArticleDOI
Learning a dictionary of prototypical grasp-predicting parts from grasping experience
TL;DR: A real-world robotic agent is presented that is capable of transferring grasping strategies across objects that share similar parts, and the applicability of the prototypical parts for grasping novel objects is demonstrated.
Journal ArticleDOI
Learning Grasp Affordance Densities
Renaud Detry,Dirk Kraft,Oliver Kroemer,Leon Bodenhagen,Jan Peters,Norbert Krüger,Justus Piater +6 more
TL;DR: This work model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability, and relies on kernel density estimation to provide a continuous model.
Proceedings ArticleDOI
Learning object-specific grasp affordance densities
Renaud Detry,Emre Baseski,Mila Popovic,Younes Touati,Norbert Krüger,Oliver Kroemer,Jan Peters,Justus Piater +7 more
TL;DR: The result of learning grasp hypothesis densities from both imitation and visual cues are shown, and grasp empirical densities learned from physical experience by a robot are presented.