Page 407 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

382 HUMAN PERFORMANCE IN MOTION PLANNING

an optimal path requires complete information about the environment
whereas our algorithms have only limited sensing information.
— One can assess algorithms’ performance theoretically. We have done
this in Chapter 3 for the case of a point robot moving in the plane. The
upper bounds on the robot performance obtained there give a good idea
about the worst-case performance of the algorithms, but they do not
answer the direct practical “How good is it in ‘normal life’?” question.
— One can attempt a comparison between paths produced by different
algorithms in the same task. While some such comparisons have been
done in literature, they are of limited value simply because different
algorithms tend to behave differently in different tasks: An algorithm
that wins in one task can easily lose in another task. That is why the
task of choosing between algorithms is a hard one. And, importantly,
such a comparison is not feasible for arm manipulators because today
there is no competing options to the sensor-based algorithms developed
in Chapters 5 and 6.
— One can compare the algorithms’ performance with human performance.
While we still don’t know what algorithms people use in such tasks,
from the practical standpoint this would be a satisfying comparison.
After all, we humans do solve motion planning problems with uncer-
tainty. We do it all the time, and so using human performance as a
benchmark would be an “apples-to-apples” comparison. We tend to
associate motion planning tasks with “thinking” and intelligence: If our
robots perform well in such tasks, we not only can be proud of the
robots’ performance but can also use this fact in technical systems.
• If human performance in motion planning tasks turns out to be less than
ideal—and the results described in this chapter demonstrate that this is so
for tasks with arm manipulators—this conclusion should pose a serious
challenge to designers of practical human-guided teleoperation systems. If
robot skills in motion planning are better than human skills, and if that is
still so after a substantial training by humans, this becomes a good argument
for a new design approach in teleoperation systems. Namely, we should
attempt to switch to human–robot synergy teams, where human intelligence
is complemented with appropriate robot intelligence.
In our tests the performance of human subjects was measured in terms of two
dependent variables:
• Path length— the length of paths a subject generates in a given task.
• Task completion time— the time a subject takes to complete a given task.

The experimental data appear in groups, each related to one independent variable
(factor). Overall, four factors have been studied:

• Task Interface: Each subject operated either a virtual arm manipulator on
the computer screen or a physical arm in the test booth.