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The work then focuses on human to robot motion mapping, introducing methods that enhance robot teleoperation through intuitive motion capture, interface design, mapping, and control. Beginning with manual robot teaching, an open-source, generic robot teaching interface is proposed and compared with alternative devices in terms of usability and efficiency. This thesis progresses through methods that require varying degrees of human involvement, proposing and evaluating approaches that facilitate manual robot teaching, teleoperation, programming by demonstration, high-level process supervision, and crowd participation. The issue of efficiently transferring human skill to robot systems is therefore becoming increasingly more important, with approaches ranging from traditional robot programming to task learning with minimal human guidance. Many of these tasks are simple and intuitive for humans, but have proven to be incredibly difficult to reliably implement on a robot system, particularly in the context of grasping and dexterous manipulation. With increasing rates of automation in industry, service, and home environments, robots are required to perform progressively more sophisticated tasks and rapidly adapt to dynamic environments.

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