Matches in SemOpenAlex for { <https://semopenalex.org/work/W3036634591> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3036634591 abstract "In order to increase the autonomy of the modern, high complexity robots with multiple degrees of freedom, it is necessary for them to be able to learn and adapt their skills, for example, using reinforcement learning (RL). However, RL performance greatly depends on the task dimensionality. Methods for reducing the task dimensionality, such as deep autoencoder neural networks, are often employed. Such neural network based dimensionality reduction approaches require a large example database for training, but obtaining such a database for a real robot is a complex and tedious process. This paper proposes a method of obtaining a database for the training of a deep autoencoder network, which serves for the dimensionality reduction of robot learning, and thus accelerates the robot’s ability to adapt to the real world. The presented method is based on a few real-world examples and statistical generalization. A comparison to using a simulated-only database on the use-case of robot throwing shows that the proposed approach achieves better real-world performance." @default.
- W3036634591 created "2020-06-25" @default.
- W3036634591 creator A5006565540 @default.
- W3036634591 creator A5019399129 @default.
- W3036634591 creator A5028393702 @default.
- W3036634591 creator A5049022354 @default.
- W3036634591 creator A5083969934 @default.
- W3036634591 date "2020-01-01" @default.
- W3036634591 modified "2023-09-25" @default.
- W3036634591 title "Generalization Based Database Acquisition for Robot Learning in Reduced Space" @default.
- W3036634591 cites W1506540549 @default.
- W3036634591 cites W1977655452 @default.
- W3036634591 cites W2012392077 @default.
- W3036634591 cites W2056114668 @default.
- W3036634591 cites W2116226448 @default.
- W3036634591 cites W2120772693 @default.
- W3036634591 cites W2136719407 @default.
- W3036634591 cites W2213467466 @default.
- W3036634591 cites W2295124130 @default.
- W3036634591 cites W2344260694 @default.
- W3036634591 cites W2914170332 @default.
- W3036634591 cites W2947346454 @default.
- W3036634591 cites W4211049957 @default.
- W3036634591 doi "https://doi.org/10.1007/978-3-030-48989-2_53" @default.
- W3036634591 hasPublicationYear "2020" @default.
- W3036634591 type Work @default.
- W3036634591 sameAs 3036634591 @default.
- W3036634591 citedByCount "0" @default.
- W3036634591 crossrefType "book-chapter" @default.
- W3036634591 hasAuthorship W3036634591A5006565540 @default.
- W3036634591 hasAuthorship W3036634591A5019399129 @default.
- W3036634591 hasAuthorship W3036634591A5028393702 @default.
- W3036634591 hasAuthorship W3036634591A5049022354 @default.
- W3036634591 hasAuthorship W3036634591A5083969934 @default.
- W3036634591 hasConcept C101738243 @default.
- W3036634591 hasConcept C111030470 @default.
- W3036634591 hasConcept C119857082 @default.
- W3036634591 hasConcept C127413603 @default.
- W3036634591 hasConcept C134306372 @default.
- W3036634591 hasConcept C154945302 @default.
- W3036634591 hasConcept C177148314 @default.
- W3036634591 hasConcept C201995342 @default.
- W3036634591 hasConcept C2780451532 @default.
- W3036634591 hasConcept C33923547 @default.
- W3036634591 hasConcept C41008148 @default.
- W3036634591 hasConcept C50644808 @default.
- W3036634591 hasConcept C70518039 @default.
- W3036634591 hasConcept C90509273 @default.
- W3036634591 hasConcept C97541855 @default.
- W3036634591 hasConceptScore W3036634591C101738243 @default.
- W3036634591 hasConceptScore W3036634591C111030470 @default.
- W3036634591 hasConceptScore W3036634591C119857082 @default.
- W3036634591 hasConceptScore W3036634591C127413603 @default.
- W3036634591 hasConceptScore W3036634591C134306372 @default.
- W3036634591 hasConceptScore W3036634591C154945302 @default.
- W3036634591 hasConceptScore W3036634591C177148314 @default.
- W3036634591 hasConceptScore W3036634591C201995342 @default.
- W3036634591 hasConceptScore W3036634591C2780451532 @default.
- W3036634591 hasConceptScore W3036634591C33923547 @default.
- W3036634591 hasConceptScore W3036634591C41008148 @default.
- W3036634591 hasConceptScore W3036634591C50644808 @default.
- W3036634591 hasConceptScore W3036634591C70518039 @default.
- W3036634591 hasConceptScore W3036634591C90509273 @default.
- W3036634591 hasConceptScore W3036634591C97541855 @default.
- W3036634591 hasLocation W30366345911 @default.
- W3036634591 hasOpenAccess W3036634591 @default.
- W3036634591 hasPrimaryLocation W30366345911 @default.
- W3036634591 hasRelatedWork W13792335 @default.
- W3036634591 hasRelatedWork W13926559 @default.
- W3036634591 hasRelatedWork W14471487 @default.
- W3036634591 hasRelatedWork W14777649 @default.
- W3036634591 hasRelatedWork W2683128 @default.
- W3036634591 hasRelatedWork W349990 @default.
- W3036634591 hasRelatedWork W4085024 @default.
- W3036634591 hasRelatedWork W547392 @default.
- W3036634591 hasRelatedWork W5991403 @default.
- W3036634591 hasRelatedWork W9860846 @default.
- W3036634591 isParatext "false" @default.
- W3036634591 isRetracted "false" @default.
- W3036634591 magId "3036634591" @default.
- W3036634591 workType "book-chapter" @default.