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Human-Inspired Balancing and Recovery Stepping for Humanoid Robots

Kaul, Lukas Sebastian

Robustly 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.

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Volltext §
DOI: 10.5445/IR/1000092689
Veröffentlicht am 26.03.2019
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Jahr 2019
Sprache Englisch
Identifikator urn:nbn:de:swb:90-926891
KITopen-ID: 1000092689
Verlag KIT, Karlsruhe
Umfang X, 235 S.
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdatum 23.01.2019
Referent/Betreuer Prof. T. Asfour
Schlagworte Humanoid Robotics, Optimal Control, Machine Learning
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