Abstract:
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. 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. 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. 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. 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. 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. On a higher level of system autonomy, a method
of enhancing programming by demonstration is proposed, utilising path optimisation and
local replanning to allow for efficient teaching and execution of assembly tasks. The work
then advances to flexible robotic assembly that requires minimal human involvement,
proposing a framework that relies on compliance control, CAD based localisation, and
a multi-modal gripper to facilitate rapid adaptation to different task requirements. Finally,
the thesis proposes a framework that leverages human perception by combining
crowdsourcing and gamification, employing it to enhance the grasping and manipulation
capabilities of assistive and autonomous robotic platforms. To evaluate the efficiency
of the developed methods, numerous experiments with different robot systems in both
structured and dynamic environments have been conducted.