  This work creates a model of the value of different external viewpoints of a
robot performing tasks. The current state of the practice is to use a
teleoperated assistant robot to provide a view of a task being performed by a
primary robot; however, the choice of viewpoints is ad hoc and does not always
lead to improved performance. This research applies a psychomotor approach to
develop a model of the relative quality of external viewpoints using Gibsonian
affordances. In this approach, viewpoints for the affordances are rated based
on the psychomotor behavior of human operators and clustered into manifolds of
viewpoints with the equivalent value. The value of 30 viewpoints is quantified
in a study with 31 expert robot operators for 4 affordances (Reachability,
Passability, Manipulability, and Traversability) using a computer-based
simulator of two robots. The adjacent viewpoints with similar values are
clustered into ranked manifolds using agglomerative hierarchical clustering.
The results show the validity of the affordance-based approach by confirming
that there are manifolds of statistically significantly different viewpoint
values, viewpoint values are statistically significantly dependent on the
affordances, and viewpoint values are independent of a robot. Furthermore, the
best manifold for each affordance provides a statistically significant
improvement with a large Cohen's d effect size (1.1-2.3) in performance
(improving time by 14%-59% and reducing errors by 87%-100%) and improvement in
performance variation over the worst manifold. This model will enable
autonomous selection of the best possible viewpoint and path planning for the
assistant robot.
