A Contribution To Resource Aware Architectures For Humanoid Robots
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A Contribution to Resource-Aware Architectures for Humanoid Robots
Author: Kroehnert, Manfred
language: en
Publisher: KIT Scientific Publishing
Release Date: 2017-05-09
The goal of this work is to provide building blocks for resource-aware robot architectures. The topic of these blocks are data-driven generation of context-sensitive resource models, prediction of future resource utilizations, and resource-aware computer vision and motion planning algorithms. The implementation of these algorithms is based on resource-aware concepts and methodologies originating from the Transregional Collaborative Research Center ""Invasive Computing"" (SFB/TR 89).
A Contribution to Resource-Aware Architectures for Humanoid Robots
The goal of this work is to provide building blocks for resource-aware robot architectures. The topic of these blocks are data-driven generation of context-sensitive resource models, prediction of future resource utilizations, and resource-aware computer vision and motion planning algorithms. The implementation of these algorithms is based on resource-aware concepts and methodologies originating from the Transregional Collaborative Research Center ""Invasive Computing"" (SFB/TR 89). This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
Author: Kaul, Lukas Sebastian
language: en
Publisher: KIT Scientific Publishing
Release Date: 2019-05-15
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.