Page 409 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

384 HUMAN PERFORMANCE IN MOTION PLANNING

The motion direction factor is not included in the table since it was added to the
study only as a secondary factor, to shed light on the primary factors’ effects.
In brief, statistical analysis of experimental data from tests with arm manipu-
lator motion planning indicates the following:

• The interface factor has no significant effect on the length of generated
paths, but has a significant effect on the task completion time.
• The visibility factor has no significant effect on human performance—neither
on the path length nor on the completion time.
• Similarly, the training factor has no significant effect on the human perfor-
mance.
• The motion direction factor has a statistically significant effect on human
performance.

Overall, these conclusions look rather surprising. Let us discuss these findings
and their implications in more detail.

Effects of the Interface Factor. The two components of this factor are the vir-
tual (simulated) interface and the physical (arm in the booth) interface. Simple
considerations and expert opinions suggest that this factor should be of much
importance. After all, we humans are used to moving physical objects. Manip-
ulating a physical object—here the arm—adds significant haptic, visual, and
even auditory information about the task. Plus, the physical arm looks much like
a human arm and hence adds to one’s confidence. On the other hand, moving an
abstract object on the screen seems far less natural. A good many observers and
participants in this study had predicted that the subjects would do much better
when moving the physical arm than when moving the virtual arm on the screen.
Indeed, statistical analysis of test data agrees to some extent with this intu-
ition: For the task completion time it does show an improvement in subjects’
performance. On the average, subjects moved the physical arm in a more contin-
uous fashion, whereas in the simulation they often paused after small motions,
spending extra time on figuring out what to do next.
However, an interesting result here was that the improvement was very small in
the path length, and that even this small effect was erased by the motion direction
factor: In Table 7.21, column “Effect on Path,” observe those “slight” and “no”
(effect) in the ANOVA left-to-right and in ANOVA right-to-left, respectively.
The fact that no significant effect of the interface factor on the path length was
found in the more difficult right-to-left task is surprising. It suggests that the
importance to a human operator of the type of interface fades as the spatial tasks
become harder. To put it bluntly, in nontrivial teleoperation motion planning
tasks the operators will likely need help, such as from the robot intelligence;
mere improvements in the control means will not go far enough.
The difference in the factor effects on the two dependent variables—path
length and completion time—is not hard to explain. The length of a path gen-
erated by a human subject is, in general, independent of how quickly or slowly