Page 361 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

336 HUMAN PERFORMANCE IN MOTION PLANNING

computer mouse. Every time the cursor approaches a labyrinth wall within some
small distance—that is your “radius of vision”—the part of the wall within this
radius becomes visible, and so you can decide where to turn to continue the
motion. Once you step back from the wall, that piece of the wall disappears from
the screen.
Your performance in this new setting will of course deteriorate compared to
the case with complete information above. You will likely wander around, hitting
dead ends and passing some segments of the path more than once. Because you
cannot now see the whole labyrinth, there will be no hope of producing a near-
optimal solution; you will struggle just to get somehow to point T .Thisis
demonstrated in two examples of tests with human subjects shown in Figure 7.3.
Among the many such samples with human subjects that were obtained in the
course of this study (see the following sections), these two are closest to the best
and worst performance, respectively. Most subjects fell somewhere in between.
While this performance is far from what we saw in the test with complete
information, it is nothing to be ashamed of—the test is far from trivial. Those
who had a chance to participate in youth wilderness training know how hard one
has to work to find a specific spot in the forest, with or without a map. And many
of us know the frustration of looking for a specific room in a large unfamiliar
building, in spite of its well-structured design.

Human Versus Computer Performance in a Labyrinth. How about com-
paring the human performance we just observed with the performance of a decent
motion planning algorithm? The computer clearly wins. For example, the Bug2
algorithm developed in Section 3.3.2, operating under the same conditions as for
the human subjects, in the version with incomplete information produces elegant
solutions shown in Figure 7.4: In case (a) the “robot” uses tactile information,
and in case (b) it uses vision, with a limited radius of vision r v , as shown.
Notice the remarkable performance of the algorithm in Figure 7.4b: The path
produced by algorithm Bug2, using very limited input information—in fact, a
fraction of complete information—almost matches the nearly optimal solution in
Figure 7.2a that was obtained with complete information.
We can only speculate about the nature of the inferior performance of humans
in motion planning with incomplete information. The examples above suggest
that humans tend to be inconsistent (one might say, lacking discipline): Some
new idea catches the eye of the subject, and he or she proceeds to try it, without
thinking much about what this change will mean for the overall outcome.
The good news is that it is quite easy to teach human subjects how to use
a good algorithm, and hence acquire consistency and discipline. With a little
practice with the Bug2 algorithm, for example, the subjects started producing
paths very similar to those shown in Figure 7.4.
This last point—that humans can easily master motion planning algorithms
for moving in a labyrinth—is particularly important. As we will see in the next
section, the situation changes dramatically when human subjects attempt motion
planning for arm manipulators. We will want to return to this comparison when