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+49 721 608 47379
asfourCun6∂kit edu

Institut für Anthropomatik und Robotik
Lehrstuhl für Hochperformante Humanoide Technologien
Adenauerring 2, Geb.50.20 76131 Karlsruhe

Sekretariat

Sprechstunde:

Mo. - Do.: 10:00h - 12:00h

Telefon: +49 721 608-43547
            +49 721 608-48277

Email: sekretariat asfourJjz8∂anthropomatik kit edu

KoroiBot: Improving humanoid walking capabilities by human-inspired mathematical models, optimization and learning

KoroiBot: Improving humanoid walking capabilities by human-inspired mathematical models, optimization and learning
Ansprechpartner:

Tamim Asfour 

Links:
Förderung:

EU-FP7

Starttermin:

2013 

Endtermin:

2016 

Description

KoroiBot is a three years project funded by the European Commission under FP7-ICT-2013-10. The goal of the project is to investigate the way humans walk, e.g., on stairs and slopes, on soft and slippery ground, over beams and seesaws and create mathematical models and learning methods for humanoid walking. The project will study human walking, develop techniques for increased humanoid walking performance and evaluate them on existing state of the art humanoid robots.

KIT leads the tasks concerning human walking experiments, the establishment of large scale human walking database and the development of human and humanoid models as basis for the development of general motion and control laws transfer rules between different embodiments and for the generation of different walking types. The developed models and transfer rules, we will study how to implement balancing push recovery strategies to deal with different types of perturbation in free and constrained situations. Furthermore, we will investigate the role of prediction in walking as well as the role different sensory feedback such as vision, vestibular and foot haptics in balancing. Therefore, we will implement a control and action selection schema emphasizing predictive control mechanisms, which rely on the estimation of expected perturbation based on multimodal sensory feedback and past sensorimotor experience. The control schema will be validated in the context of prediction and selection of push recovery actions.