The SARAFun project has been formed to enable a non-expert user to integrate a new bi-manual assembly task on a robot in less than a day. This will be accomplished by augmenting the robot with cutting edge sensory and cognitive abilities as well as reasoning abilities required to plan and execute an assembly task.
“Scaffolding” is a powerful principle taken from psychology for organizing and optimizing the progress of a learner. The goal in this project is to combine this powerful structuring of a learning process with recent advances in deep and hierarchical reinforcement learning in order to make learning of an artificial agent more efficient and feasible when interaction data is much more limited than considered in current studies.
Clothing provides a challenging test domain for research in the field of cognitive robotics. On the one hand, robots have to make use of commonsense knowledge to be able to understand the socially constructed meaning and function of garments. On the other hand, the variance resulting from deformations and differences between individual items of clothing calls for implicit representations which have to be learned from experience. Our robot uses topological, geometric, and subsymbolic knowledge representations for the manipulation of clothes with its anthropomorphic hands. read more »
In the project REBA+, funded within DFG priority program "Autonomous Learning", we develop, implement and evaluate rich extensions of a robot's body schema, along with learning algorithms that use these representations as strong priors in order to enable rapid and autonomous usage of tools and a flexible coping with novel mechanical linkages between the body, the grasped tool and target objects.
Which relations exist between properties of animals or people and their kinematic patterns? For example, can we tell, who performed a hand-over of which kind of object under which conditions just by looking at the sequence of joint angles? We try to find answers to these questions by employing a 3D motion tracking system.
Motivated by the need of complex anthropomorphic robots to manage sophisticated spatial relationships between parts of their body and the environment, together with recent findings from the neurosciences about how the brain solves this challenge by means of a highly adaptable “body schema”, the present proposal pursues the goal of
The SARAFun project has been formed to enable a non-expert user to integrate a new bi-manual assembly task on a robot in less than a day. This will be accomplished by augmenting the robot with cutting edge sensory and cognitive abilities as well as reasoning abilities required to plan and execute an assembly task.
Clothing provides a challenging test domain for research in the field of cognitive robotics. On the one hand, robots have to make use of commonsense knowledge to be able to understand the socially constructed meaning and function of garments. On the other hand, the variance resulting from deformations and differences between individual items of clothing calls for implicit representations which have to be learned from experience. Our robot uses topological, geometric, and subsymbolic knowledge representations for the manipulation of clothes with its anthropomorphic hands. 
In the project REBA+, funded within DFG priority program "Autonomous Learning", we develop, implement and evaluate rich extensions of a robot's body schema, along with learning algorithms that use these representations as strong priors in order to enable rapid and autonomous usage of tools and a flexible coping with novel mechanical linkages between the body, the grasped tool and target objects. 
Which relations exist between properties of animals or people and their kinematic patterns? For example, can we tell, who performed a hand-over of which kind of object under which conditions just by looking at the sequence of joint angles? We try to find answers to these questions by employing a 3D motion tracking system.