Robot hands are one of the most important but also most complex parts of a robot system. In the field of soft robotics, one goal is to design robot hands that resemble the human hand and can adapt their capabilities. Especially important is the ability to manipulate objects in the hand, the so-called in-hand manipulation. But to carry out in-hand manipulation complex movements are required. In order to be able to execute these movements, we would like to teleoperate the RBO-Hand 3 with a data glove.
The aim of this thesis is to remotely control a soft pneumatically operated robot hand developed by the department with the help of a data glove in order to be able to carry out in-hand manipulations.
One part of the work is to create a mapping between the data glove and the RBO-Hand 3. Supervised learning algorithms are used to learn the mapping. The supervised learning shall be based on data recorded with the data glove. Another part of the work is to use the learned mapping to teleoperate the RBO-Hand 3 by means of the data glove. Subsequently, the resulting teleoperation tool will be used to perform experiments on in-hand manipulation with the RBO-Hand 3.