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Human-Inspired Balancing and Recovery Stepping for Humanoid Robots
Abstract
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- doc-type:doctoralThesis
- Text
- info:eu-repo/semantics/doctoralThesis
- dissertation
- info:eu-repo/semantics/publishedVersion
- Humanoide Robotik
- Regelungstechnik
- Maschinelles Lernen
- Balancieren
- Optimierung
- Humanoid robotics
- Control systems
- Machine learning
- Balancing
- Optimization
- ddc:004
- DATA processing & computer science
- info:eu-repo/classification/ddc/004