Page 370 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

PRELIMINARY OBSERVATIONS 345

positions S and T and its current position. The arm’s sensing is assumed to
allow the arm to sense surrounding objects at every point of its body, within
some modest distance r v from that point. In Figure 7.8, radius r v is equal to
about half of the link l 1 thickness; such sensing is readily achievable today in
practice (see Chapter 8).
Similar to Figure 7.6, the resulting path in Figure 7.8 (dotted line) is the path
traversed by the arm endpoint when moving from position S to position T .
Recall that the algorithm takes as its base path (called M-line) one of the four
possible “shortest” straight lines in the arm’s C-space (see lines M 1 ,M 2 ,M 3 ,M 4
in Figure 5.5); distances and path lengths are measured in C-space in radians.
In the example in Figure 7.8, the shortest of these four is chosen (it is shown as
line M1, a dashed line). In other words, if no obstacles were present, under the
algorithm the arm endpoint would have moved along the curve M1; given the
obstacles, it went along the dotted line path.
The elegant algorithm-generated path in Figure 7.8 is not only shorter than
those generated by human subjects (Figure 7.6). Notice the dramatic differ-
ence between the corresponding (human versus computer) arm test and the
labyrinth test. While a path produced in the labyrinth by the computer algorithm
(Figure 7.4) presents no conceptual difficulty for an average human subject, they
find the path in Figure 7.8 incomprehensible. What is the logic behind those
sweeping curves? Is this a good way to move the arm from S to T ? The best
way? Consequently, while human subjects can easily master the algorithm in the
labyrinth case, they find it hard—in fact, seemingly impossible—to make use
of the algorithm for the arm manipulator.

7.2.3 Conclusions and Plan for Experiment Design
We will now summarize the observations made in the previous section, and will
pose a few questions that will help us design a more comprehensive study of
human cognitive skills in space reasoning and motion planning:

1. The labyrinth test is a good easy-case benchmark for testing one’s general
space reasoning abilities, and it should be included in the battery of tests.
There are a few reasons for this: (a) If a person finds it difficult to move
in the labyrinth—which happens rarely—he or she will be unlikely to
handle the arm manipulator test. (b) The labyrinth test prepares a subject
for the test with an easier task, making the switch to the arm test more
gradual. (c) A subject’s successful operation in the labyrinth test suggests
that whatever difficulty the subject may have with the arm test, it likely
relates to the subject’s cognitive difficulties rather than to the test design
or test protocol.
2. When moving the arm, subjects exhibit different tastes for control means:
Some subjects, for example, prefer to change both joint angles simulta-
neously, “pulling” the arm endpoint in the direction they desire, whereas
other subjects prefer to move one joint at the time, thus producing circular