Matches in SemOpenAlex for { <https://semopenalex.org/work/W4211147391> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4211147391 endingPage "4243" @default.
- W4211147391 startingPage "4236" @default.
- W4211147391 abstract "The safety of human workers has been the main concern in human-robot close collaboration. Along with rapidly developed artificial intelligence techniques, deep learning models using two-dimensional images have become feasible solutions for human motion detection. These models serve as “sensors” in the closed-loop system that involve humans and robots. Most existing methods that detect human motion using images do not consider the uncertainty from the deep learning model itself. The mappings established by deep learning models should not be taken blindly, and thus uncertainty should be a natural part of this type of sensor. In particular, model uncertainty should be explicitly quantified and incorporated into robot motion control to guarantee safety. With this motivation, to rigorously quantify the uncertainty of these “sensors”, this letter proposes a probabilistic interpretation method and automatically provides a framework to benefit from a deep model's uncertainty. Experimental data from human-robot collaboration has been collected and used to validate the proposed method. A training strategy is proposed to efficiently train surrogate models that learn to refine the prediction of the main Bayesian models. The proposed framework is also compared with Ego hands benchmark showing a 4.7% increase in mIoU." @default.
- W4211147391 created "2022-02-13" @default.
- W4211147391 creator A5014259384 @default.
- W4211147391 creator A5025952499 @default.
- W4211147391 creator A5042825819 @default.
- W4211147391 creator A5066836550 @default.
- W4211147391 creator A5084538397 @default.
- W4211147391 creator A5086412282 @default.
- W4211147391 date "2022-04-01" @default.
- W4211147391 modified "2023-10-11" @default.
- W4211147391 title "Uncertainty-Assisted Image-Processing for Human-Robot Close Collaboration" @default.
- W4211147391 cites W2006347227 @default.
- W4211147391 cites W2110987513 @default.
- W4211147391 cites W2111327178 @default.
- W4211147391 cites W2119656522 @default.
- W4211147391 cites W2131404784 @default.
- W4211147391 cites W2140190675 @default.
- W4211147391 cites W2149085596 @default.
- W4211147391 cites W2154777391 @default.
- W4211147391 cites W2169706473 @default.
- W4211147391 cites W2204609240 @default.
- W4211147391 cites W2293753755 @default.
- W4211147391 cites W2406081291 @default.
- W4211147391 cites W2559597482 @default.
- W4211147391 cites W2728530863 @default.
- W4211147391 cites W2743137872 @default.
- W4211147391 cites W2768491079 @default.
- W4211147391 cites W2793432429 @default.
- W4211147391 cites W2795899404 @default.
- W4211147391 cites W287022735 @default.
- W4211147391 cites W2887662010 @default.
- W4211147391 cites W2898607715 @default.
- W4211147391 cites W2905111988 @default.
- W4211147391 cites W2919115771 @default.
- W4211147391 cites W2924560602 @default.
- W4211147391 cites W2941651142 @default.
- W4211147391 cites W2982161360 @default.
- W4211147391 cites W2995301359 @default.
- W4211147391 cites W3037946796 @default.
- W4211147391 cites W3046175593 @default.
- W4211147391 cites W4205123165 @default.
- W4211147391 cites W639708223 @default.
- W4211147391 doi "https://doi.org/10.1109/lra.2022.3150487" @default.
- W4211147391 hasPublicationYear "2022" @default.
- W4211147391 type Work @default.
- W4211147391 citedByCount "11" @default.
- W4211147391 countsByYear W42111473912022 @default.
- W4211147391 countsByYear W42111473912023 @default.
- W4211147391 crossrefType "journal-article" @default.
- W4211147391 hasAuthorship W4211147391A5014259384 @default.
- W4211147391 hasAuthorship W4211147391A5025952499 @default.
- W4211147391 hasAuthorship W4211147391A5042825819 @default.
- W4211147391 hasAuthorship W4211147391A5066836550 @default.
- W4211147391 hasAuthorship W4211147391A5084538397 @default.
- W4211147391 hasAuthorship W4211147391A5086412282 @default.
- W4211147391 hasConcept C108583219 @default.
- W4211147391 hasConcept C119857082 @default.
- W4211147391 hasConcept C13280743 @default.
- W4211147391 hasConcept C154945302 @default.
- W4211147391 hasConcept C185798385 @default.
- W4211147391 hasConcept C205649164 @default.
- W4211147391 hasConcept C41008148 @default.
- W4211147391 hasConcept C49937458 @default.
- W4211147391 hasConcept C90509273 @default.
- W4211147391 hasConceptScore W4211147391C108583219 @default.
- W4211147391 hasConceptScore W4211147391C119857082 @default.
- W4211147391 hasConceptScore W4211147391C13280743 @default.
- W4211147391 hasConceptScore W4211147391C154945302 @default.
- W4211147391 hasConceptScore W4211147391C185798385 @default.
- W4211147391 hasConceptScore W4211147391C205649164 @default.
- W4211147391 hasConceptScore W4211147391C41008148 @default.
- W4211147391 hasConceptScore W4211147391C49937458 @default.
- W4211147391 hasConceptScore W4211147391C90509273 @default.
- W4211147391 hasFunder F4320335353 @default.
- W4211147391 hasIssue "2" @default.
- W4211147391 hasLocation W42111473911 @default.
- W4211147391 hasOpenAccess W4211147391 @default.
- W4211147391 hasPrimaryLocation W42111473911 @default.
- W4211147391 hasRelatedWork W2922457425 @default.
- W4211147391 hasRelatedWork W3014300295 @default.
- W4211147391 hasRelatedWork W3164822677 @default.
- W4211147391 hasRelatedWork W4223943233 @default.
- W4211147391 hasRelatedWork W4225161397 @default.
- W4211147391 hasRelatedWork W4250304930 @default.
- W4211147391 hasRelatedWork W4309045103 @default.
- W4211147391 hasRelatedWork W4312200629 @default.
- W4211147391 hasRelatedWork W4360585206 @default.
- W4211147391 hasRelatedWork W4364306694 @default.
- W4211147391 hasVolume "7" @default.
- W4211147391 isParatext "false" @default.
- W4211147391 isRetracted "false" @default.
- W4211147391 workType "article" @default.