Page 386 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

RESULTS—EXPERIMENT ONE 361

agrees with our intuition—that an opposite is true in the point-in-the-labyrinth
test: One performs significantly better with a bird’s-eye view of the labyrinth
than when seeing at each moment only a small part of the labyrinth.
Apparently, something changes dramatically when one switches from the
labyrinth test to moving a kinematic structure, the arm test. With the arm, mul-
tiple points of the arm body are subject to collision, the contacts may happen
simultaneously, and the relation between some such points keeps changing as
the arm links move relative to each other. It is likely (and some of our tests
confirm this) that in simpler tasks where the arm cannot touch more than one
obstacle at the time the visibility factor will play a role similar to the labyrinth
test. It is clear, however, that if our more general result holds after a sufficient
training of subjects (see Experiment Two below), we cannot rely on operator’s
skills in more complex tasks of robot arm teleoperation. Providing the robot with
more intelligence—perhaps of the kind developed in Chapters 5 and 6—will be
necessary to successfully handle teleoperation tasks.
3. The results of testing the effect of interface on the simulation group data
and the booth group data are shown in Table 7.5; here, Virt stands for “virtual”
and Phys stands for “physical.” Given the significance level p< 0.01, we reject
the null hypothesis (which says the two group data sets came from the same pop-
ulation). We therefore conclude that there is a statistically significant difference
between the virtual tests (tests where subjects move the arm on the computer
screen) and “physical” tests (tests where subjects move the physical arm). In
other words, the interface factor has a statistically significant effect on the length
of paths produced by the subjects. Furthermore, this effect is present whether or
not the task is implemented in a visible or invisible environment, and whether
or not the direction of motion is left-to-right or right-to-left.
While the Mann–Whitney statistical test isolates the single factor we are
interested in, the interface factor, its results do not reconcile easily with the
observations summarized in Table 7.1. Namely, Table 7.1 shows that while in
the easier (left-to-right) task the subjects performed better with the physical arm
than with the virtual arm, this difference practically disappeared in the harder
(right-to-left) task. This calls for more refined statistical tests, with two separate
direction-of-motion data sets. These are summarized next.
4. Here the Mann–Whitney test measures the effect of the interface factor
using only the left-to-right (LtoR) data sets. The results are shown in Table 7.6.
Given the significance level p< 0.01, we reject the null hypothesis and conclude
that in the left-to-right task there is a statistically significant difference between

TABLE 7.5. Results of Mann–Whitney Test on the Interface Factor
Mann–Whitney Test Variable: Interface. Group 1: Virt; Group 2: Phys
Rank Sum Valid N
Variable Virt Phys U p-Level Virt Phys
Path length 10427.00 7718.000 3253.000 0.000895 96 94