Kroemer B.O. Machine Learning for Robot Grasping and Manipulation

Technical University of Darmstadt, 2014. — 149 p.Robotics as a technology has an incredible potential for improving our everyday lives. Robots could perform household chores, such as cleaning, cooking, and gardening, in order to give us more time for other pursuits. Robots could also be used to perform tasks in hazardous environments, such as turning off a valve in an emergency or safely sorting our more dangerous trash. However, all of these applications would require the robot to perform manipulation tasks with various objects. Today's robots are used primarily for performing specialized tasks in controlled scenarios, such as manufacturing. The robots that are used in today's applications are typically designed for a single purpose and they have been preprogrammed with all of the necessary task information. In contrast, a robot working in a more general environment will often be confronted with new objects and scenarios. Therefore, in order to reach their full potential as autonomous physical agents, robots must be capable of learning versatile manipulation skills for different objects and situations. Hence, we have worked on a variety of manipulation skills to improve those capabilities of robots, and the results have lead to several new approaches, which are presented in this thesis.