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The goal of this work is the development of a novel computational formalization of whole-body affordances which is suitable for the multimodal detection and validation of interaction possibilities in unknown environments. The hierarchical framework allows the consistent fusion of affordance-related evidence and can be utilized for realizing shared autonomous control of humanoid robots. The affordance formalization is evaluated in several experiments in simulation and on real humanoid robots.
Kognition --- cognition --- Manipulation --- Perzeption --- robotics --- Humanoide Robotik --- humanoid robotics --- manipulation --- Robotik --- perception
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Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations.
Autonomous systems --- Graphical programming --- Humanoide Robotik --- Programming by demonstration --- Autonome Systeme --- Robotik --- Programmieren durch Vormachen --- Robotics --- Humanoid robotics --- Graphische Programmierung
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