  Autonomous robots have real-world applications in diverse fields, such as
mobile manipulation and environmental exploration, and many such tasks benefit
from a hands-off approach in terms of human user involvement over a long task
horizon. However, the level of autonomy achievable by a deployment is limited
in part by the problem definition or task specification required by the system.
Task specifications often require technical, low-level information that is
unintuitive to describe and may result in generic solutions, burdening the user
technically both before and after task completion. In this thesis, we aim to
advance task specification abstraction toward the goal of increasing robot
autonomy in real-world scenarios. We do so by tackling problems that address
several different angles of this goal. First, we develop a way for the
automatic discovery of optimal transition points between subtasks in the
context of constrained mobile manipulation, removing the need for the human to
hand-specify these in the task specification. We further propose a way to
automatically describe constraints on robot motion by using demonstrated data
as opposed to manually-defined constraints. Then, within the context of
environmental exploration, we propose a flexible task specification framework,
requiring just a set of quantiles of interest from the user that allows the
robot to directly suggest locations in the environment for the user to study.
We next systematically study the effect of including a robot team in the task
specification and show that multirobot teams have the ability to improve
performance under certain specification conditions, including enabling
inter-robot communication. Finally, we propose methods for a communication
protocol that autonomously selects useful but limited information to share with
the other robots.
