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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.

Maschinelles Lernen --- Balancing --- Optimierung --- Regelungstechnik --- Machine learning --- Balancieren --- Control systems --- Humanoide Robotik --- Humanoid robotics --- Optimization

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Visual perception is one of the most important sources of information for both humans and robots. A particular challenge is the acquisition and interpretation of complex unstructured scenes. This work contributes to active vision for humanoid robots. A semantic model of the scene is created, which is extended by successively changing the robot's view in order to explore interaction possibilities of the scene.

Maths for computer scientists --- Roboter --- Humanoide Robotik --- robots --- Robots --- humanoid robots

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This work presents an approach to data-driven motion generation for humanoid robots, which is based on the observation and analysis of human whole-body motions. To this end, we investigate how captured human motions can be represented, classified and organized in a large-scale motion database. The statistical modeling of the transitions between characteristic whole-body poses enables the subsequent generation of multi-contact motions.

Ganzkörperbewegung --- robotics --- motion generation --- Bewegungsgenerierung --- menschliche Bewegungsanalyse --- humanoid robotics --- human motion analysis --- Robotik --- humanoide Robotik --- whole-body motion