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dc.contributor.authorKaul, Lukas Sebastian*
dc.date.accessioned2021-02-11T15:38:06Z
dc.date.available2021-02-11T15:38:06Z
dc.date.issued2019*
dc.date.submitted2019-07-30 20:01:57*
dc.identifier34403*
dc.identifier.issn25120875*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/49648
dc.description.abstractRobustly maintaining balance on two legs is an important challenge for humanoid robots. The work presented in this book represents a contribution to this area. It investigates efficient methods for the decision-making from internal sensors about whether and where to step, several improvements to efficient whole-body postural balancing methods, and proposes and evaluates a novel method for efficient recovery step generation, leveraging human examples and simulation-based reinforcement learning.*
dc.languageEnglish*
dc.relation.ispartofseriesKarlsruhe Series on Humanoid Robotics*
dc.subjectQA75.5-76.95*
dc.subject.otherMaschinelles Lernen*
dc.subject.otherBalancing*
dc.subject.otherOptimierung*
dc.subject.otherRegelungstechnik*
dc.subject.otherMachine learning*
dc.subject.otherBalancieren*
dc.subject.otherControl systems*
dc.subject.otherHumanoide Robotik*
dc.subject.otherHumanoid robotics*
dc.subject.otherOptimization*
dc.titleHuman-Inspired Balancing and Recovery Stepping for Humanoid Robots*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000091605*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731509035*
oapen.pagesX, 235 p.*
oapen.volume5*


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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-sa/4.0/