  Teleoperation of heavy machinery in industry often requires operators to be
in close proximity to the plant and issue commands on a per-actuator level
using joystick input devices. However, this is non-intuitive and makes
achieving desired job properties a challenging task requiring operators to
complete extensive and costly training. Despite this, operator fatigue is
common with implications for personal safety, project timeliness, cost, and
quality. While full automation is not yet achievable due to unpredictability
and the dynamic nature of the environment and task, shared control paradigms
allow operators to issue high-level commands in an intuitive, task-informed
control space while having the robot optimize for achieving desired job
properties.
  In this paper, we compare a number of modes of teleoperation, exploring both
the number of dimensions of the control input as well as the most intuitive
control spaces. Our experimental evaluations of the performance metrics were
based on quantifying the difficulty of tasks based on the well known Fitts' law
as well as a measure of how well constraints affecting the task performance
were met. Our experiments show that higher performance is achieved when humans
submit commands in low-dimensional task spaces as opposed to joint space
manipulations.
