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Humanoid robots operating in human-centered environments should be able to autonomously acquire knowledge about the environment and the objects encountered in it as well as their physical body.

The work in this area deals with the integration of proprioceptive and tactile information from the sensor system of the hand with visual information to acquire rich object representations of unknown objects which may enhance the recognition performance. Haptic and visual exploration strategies are investigated to guide the robot hand along the surface of potential object candidates.

In addition, we are investigating how knowledge about the robot's geometry and kinematic parameters can be learned to facilitate autonomous recalibration when the robot's physical body properties changed, especially concerning the end effector.