Human-Inspired Balancing and Recovery Stepping for Humanoid Robots

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 eff...

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1. autor: Kaul, Lukas Sebastian
Format: Online
Język:angielski
Wydane: KIT Scientific Publishing 2021
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Dostęp online:34403
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author Kaul, Lukas Sebastian
author_browse Kaul, Lukas Sebastian
author_facet Kaul, Lukas Sebastian
author_sort Kaul, Lukas Sebastian
collection Directory of Open Access Books
description 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.
format Online
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institution Directory of Open Access Books
language eng
publishDate 2021
publishDateRange 2021
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spelling doab-20.500.12854ir-496482023-12-20T18:40:50Z Human-Inspired Balancing and Recovery Stepping for Humanoid Robots Kaul, Lukas Sebastian QA75.5-76.95 Maschinelles Lernen Balancing Optimierung Regelungstechnik Machine learning Balancieren Control systems Humanoide Robotik Humanoid robotics Optimization bic Book Industry Communication::U Computing & information technology::UY Computer science 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. 2021-02-11T15:38:06Z 2021-02-11T15:38:06Z 2019-07-30 20:01:57 2019 book 34403 25120875 9783731509035 https://directory.doabooks.org/handle/20.500.12854/49648 eng Karlsruhe Series on Humanoid Robotics image/jpeg Attribution-ShareAlike 4.0 International https://www.ksp.kit.edu/9783731509035 KIT Scientific Publishing 10.5445/KSP/1000091605 10.5445/KSP/1000091605 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731509035 X, 235 p. open access
spellingShingle QA75.5-76.95
Maschinelles Lernen
Balancing
Optimierung
Regelungstechnik
Machine learning
Balancieren
Control systems
Humanoide Robotik
Humanoid robotics
Optimization
bic Book Industry Communication::U Computing & information technology::UY Computer science
Kaul, Lukas Sebastian
Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title_full Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title_fullStr Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title_full_unstemmed Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title_short Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
title_sort human inspired balancing and recovery stepping for humanoid robots
topic QA75.5-76.95
Maschinelles Lernen
Balancing
Optimierung
Regelungstechnik
Machine learning
Balancieren
Control systems
Humanoide Robotik
Humanoid robotics
Optimization
bic Book Industry Communication::U Computing & information technology::UY Computer science
topic_facet QA75.5-76.95
Maschinelles Lernen
Balancing
Optimierung
Regelungstechnik
Machine learning
Balancieren
Control systems
Humanoide Robotik
Humanoid robotics
Optimization
bic Book Industry Communication::U Computing & information technology::UY Computer science
url 34403
work_keys_str_mv AT kaullukassebastian humaninspiredbalancingandrecoverysteppingforhumanoidrobots