FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2953669419> ?p ?o ?g. }

• W2953669419 endingPage "201" @default.
• W2953669419 startingPage "183" @default.
• W2953669419 abstract "We present a novel approach to perform object-independent grasp synthesis from depth images via deep neural networks. Our generative grasping convolutional neural network (GG-CNN) predicts a pixel-wise grasp quality that can be deployed in closed-loop grasping scenarios. GG-CNN overcomes shortcomings in existing techniques, namely discrete sampling of grasp candidates and long computation times. The network is orders of magnitude smaller than other state-of-the-art approaches while achieving better performance, particularly in clutter. We run a suite of real-world tests, during which we achieve an 84% grasp success rate on a set of previously unseen objects with adversarial geometry and 94% on household items. The lightweight nature enables closed-loop control of up to 50 Hz, with which we observed 88% grasp success on a set of household objects that are moved during the grasp attempt. We further propose a method combining our GG-CNN with a multi-view approach, which improves overall grasp success rate in clutter by 10%. Code is provided at https://github.com/dougsm/ggcnn" @default.
• W2953669419 created "2019-07-12" @default.
• W2953669419 creator A5002099862 @default.
• W2953669419 creator A5023679097 @default.
• W2953669419 creator A5080187476 @default.
• W2953669419 date "2019-06-26" @default.
• W2953669419 modified "2023-10-18" @default.
• W2953669419 title "Learning robust, real-time, reactive robotic grasping" @default.
• W2953669419 cites W1562076358 @default.
• W2953669419 cites W1820657498 @default.
• W2953669419 cites W1892339738 @default.
• W2953669419 cites W1903029394 @default.
• W2953669419 cites W1978580730 @default.
• W2953669419 cites W1989972452 @default.
• W2953669419 cites W1999156278 @default.
• W2953669419 cites W1999488268 @default.
• W2953669419 cites W2005824379 @default.
• W2953669419 cites W2041376653 @default.
• W2953669419 cites W2118262422 @default.
• W2953669419 cites W2146922385 @default.
• W2953669419 cites W2156219259 @default.
• W2953669419 cites W2157094900 @default.
• W2953669419 cites W2167501464 @default.
• W2953669419 cites W2171066085 @default.
• W2953669419 cites W2194775991 @default.
• W2953669419 cites W2201912979 @default.
• W2953669419 cites W2300687442 @default.
• W2953669419 cites W2414685554 @default.
• W2953669419 cites W2485911221 @default.
• W2953669419 cites W2561266087 @default.
• W2953669419 cites W2600030077 @default.
• W2953669419 cites W2604698059 @default.
• W2953669419 cites W2737420227 @default.
• W2953669419 cites W2773993404 @default.
• W2953669419 cites W2914944935 @default.
• W2953669419 cites W2962737955 @default.
• W2953669419 cites W2962746398 @default.
• W2953669419 cites W2962793652 @default.
• W2953669419 cites W2962875890 @default.
• W2953669419 cites W2963033241 @default.
• W2953669419 cites W2963326767 @default.
• W2953669419 cites W2963394817 @default.
• W2953669419 cites W2963785592 @default.
• W2953669419 cites W2964137828 @default.
• W2953669419 cites W3099587965 @default.
• W2953669419 cites W3112422759 @default.
• W2953669419 cites W4241088297 @default.
• W2953669419 doi "https://doi.org/10.1177/0278364919859066" @default.
• W2953669419 hasPublicationYear "2019" @default.
• W2953669419 type Work @default.
• W2953669419 sameAs 2953669419 @default.
• W2953669419 citedByCount "180" @default.
• W2953669419 countsByYear W29536694192019 @default.
• W2953669419 countsByYear W29536694192020 @default.
• W2953669419 countsByYear W29536694192021 @default.
• W2953669419 countsByYear W29536694192022 @default.
• W2953669419 countsByYear W29536694192023 @default.
• W2953669419 crossrefType "journal-article" @default.
• W2953669419 hasAuthorship W2953669419A5002099862 @default.
• W2953669419 hasAuthorship W2953669419A5023679097 @default.
• W2953669419 hasAuthorship W2953669419A5080187476 @default.
• W2953669419 hasBestOaLocation W29536694191 @default.
• W2953669419 hasConcept C11413529 @default.
• W2953669419 hasConcept C119857082 @default.
• W2953669419 hasConcept C132094186 @default.
• W2953669419 hasConcept C154945302 @default.
• W2953669419 hasConcept C166957645 @default.
• W2953669419 hasConcept C171268870 @default.
• W2953669419 hasConcept C177264268 @default.
• W2953669419 hasConcept C199360897 @default.
• W2953669419 hasConcept C2781238097 @default.
• W2953669419 hasConcept C31972630 @default.
• W2953669419 hasConcept C41008148 @default.
• W2953669419 hasConcept C45374587 @default.
• W2953669419 hasConcept C554190296 @default.
• W2953669419 hasConcept C76155785 @default.
• W2953669419 hasConcept C79581498 @default.
• W2953669419 hasConcept C81363708 @default.
• W2953669419 hasConcept C90509273 @default.
• W2953669419 hasConcept C95457728 @default.
• W2953669419 hasConceptScore W2953669419C11413529 @default.
• W2953669419 hasConceptScore W2953669419C119857082 @default.
• W2953669419 hasConceptScore W2953669419C132094186 @default.
• W2953669419 hasConceptScore W2953669419C154945302 @default.
• W2953669419 hasConceptScore W2953669419C166957645 @default.
• W2953669419 hasConceptScore W2953669419C171268870 @default.
• W2953669419 hasConceptScore W2953669419C177264268 @default.
• W2953669419 hasConceptScore W2953669419C199360897 @default.
• W2953669419 hasConceptScore W2953669419C2781238097 @default.
• W2953669419 hasConceptScore W2953669419C31972630 @default.
• W2953669419 hasConceptScore W2953669419C41008148 @default.
• W2953669419 hasConceptScore W2953669419C45374587 @default.
• W2953669419 hasConceptScore W2953669419C554190296 @default.
• W2953669419 hasConceptScore W2953669419C76155785 @default.
• W2953669419 hasConceptScore W2953669419C79581498 @default.
• W2953669419 hasConceptScore W2953669419C81363708 @default.
• W2953669419 hasConceptScore W2953669419C90509273 @default.
• W2953669419 hasConceptScore W2953669419C95457728 @default.

• next