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.
One central issue for the cognitive control of movement is the compensation of errors and learning processes that enhance error compensation mechanisms. This is especially true for very precise movements such as many manual actions.
The present project combines methods and conventional experimental settings (first order reality) with approaches from Virtual Reality and Augmented Reality to embed subjects in interaction loops in which the occurrence and perception of errors can be manipulated and studied in novel ways. In this way we hope to gain new clues about error correction mechanisms, error compensation learning and their replication in technical systems such as robots. read more »
What insights can we gain from psychological measurements of biomechanical parameters and subjective judgements of manual actions (like object grasping) about the structures of the underlying cognitive representations? In this project, we will bring together statistical methods (like structure dimensional and principal components analysis) with
connectionist approaches employing artificial neural networks to test different hypotheses about the cognitive structure of manual actions. A major goal will be to emulate and control grasping behavior for a broad range of objects in kinematic simulations and - as a longer term objective - in real physics on a robot platform. read more »
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.