Teleoperation is an integral component of various industrial
processes. For example, concrete spraying, assisted welding,
plastering, inspection, and maintenance. Often these systems implement
direct control that maps interface signals onto robot
motions. Successful completion of tasks typically requires high levels
of manual dexterity and cognitive load. In addition, the operator is
often present nearby dangerous machinery. Consequently, safety is of
critical importance and training is expensive and prolonged -- in some
cases taking several months or even years. An autonomous robot
replacement would be an ideal solution since the human could be
removed from danger and training costs significantly reduced. However,
this is currently not possible due to the complexity and
unpredictability of the environments, and the levels of situational
and contextual awareness required to successfully complete these
tasks. In this thesis, the limitations of direct control are addressed
by developing methods for shared autonomy. A shared autonomous
approach combines human input with autonomy to generate optimal robot
motions. The approach taken in this thesis is to formulate shared
autonomy within an optimization framework that finds optimized states
and controls by minimizing a cost function, modeling task objectives,
given a set of (changing) physical and operational constraints. Online
shared autonomy requires the human to be continuously interacting with
the system via an interface (akin to direct control). The key
challenges addressed in this thesis are: 1) ensuring computational
feasibility (such a method should be able to find solutions fast
enough to achieve a sampling frequency bound below by 40Hz), 2) being
reactive to changes in the environment and operator intention, 3)
knowing how to appropriately blend operator input and autonomy, and 4)
allowing the operator to supply input in an intuitive manner that is
conducive to high task performance. Various operator interfaces are
investigated with regards to the control space, called a mode of
teleoperation. Extensive evaluations were carried out to determine for
which modes are most intuitive and lead to highest performance in
target acquisition tasks (e.g. spraying/welding/etc). Our performance
metrics quantified task difficulty based on Fitts' law, as well as a
measure of how well constraints affecting the task performance were
met. The experimental evaluations indicate that higher performance is
achieved when humans submit commands in low-dimensional task spaces as
opposed to joint space manipulations. In addition, our multivariate
analysis indicated that those with regular exposure to computer games
achieved higher performance. Shared autonomy aims to relieve human
operators of the burden of precise motor control, tracking, and
localization. An optimization-based representation for shared autonomy
in dynamic environments was developed. Real-time tractability is
ensured by modulating the human input with information of the changing
environment within the same task space, instead of adding it to the
optimization cost or constraints. The method was illustrated with two
real world applications: grasping objects in cluttered environments
and spraying tasks requiring sprayed linings with greater
homogeneity. Maintaining motion patterns -- referred to as skills --
is often an integral part of teleoperation for various industrial
processes (e.g. spraying, welding, plastering). We develop a novel
model-based shared autonomous framework for incorporating the notion
of skill assistance to aid operators to sustain these motion patterns
whilst adhering to environment constraints. In order to achieve
computational feasibility, we introduce a novel parameterization for
state and control that combines skill and underlying trajectory
models, leveraging a special type of curve known as Clothoids. This
new parameterization allows for efficient computation of skill-based
short term horizon plans, enabling the use of a model predictive
control loop. Our hardware realization validates the effectiveness of
our method to recognize a change of intended skill, and showing an
improved quality of output motion, even under dynamically changing
obstacles. In addition, extensions of the work to supervisory control
are described. An exploratory study presents an approach that improves
computational feasibility for complex tasks with minimal interactive
effort on the part of the human. Adaptations are theorized which might
allow such a method to be applicable and beneficial to high degree of
freedom systems. Finally, a system developed in our lab is described
that implements sliding autonomy and shown to complete multi-objective
tasks in complex environments with minimal interaction from the human.