Page 34 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

INTRODUCTION 9

universality. This is not to say that a robot capable of moving dirty dishes from
the table to a dishwasher will be as skillful in cutting dead limbs from trees.
The higher universality applies only to the fact that the problem of handling
uncertainty is quite generic in different applications. That is, different robots will
likely use very similar mechanisms for collision avoidance. A robot that collects
dishes from the table can use the same basic mechanism for collision avoidance
as a robot that cuts dead limbs from trees.
As said above, we are not there yet with commercial machines of this kind.
The last 40 years of robotics witnessed a slow and rather painful progress—much
slower, for example, than the progress in computers. Things turned out to be much
harder than many of us expected. Still, today’s robots in automation-intensive
industries are highly sophisticated. What is needed is supplying them with an
ability to survive in an unstructured world. There are obvious examples show-
ing what this can give. We would not doubt, for example, that, other issues
aside, a robot can move a scalpel inside a patient’s skull with more precision
than a human surgeon, thus allowing a smaller hole in the skull compared to a
conventional operation. But, an operating room is a highly unstructured environ-
ment. To be useful rather than to be a nuisance or a danger, the robot has to be
“environment-hardened.”
There is another interesting side to robot motion planning. Some intriguing
examples suggest that it is not always true that robots are worse than people
in space reasoning and motion planning. Observations show that human opera-
tors whose task is to plan and control complex motion—for example, guide the
Space Shuttle arm manipulator—make mistakes that translate into costly repairs.
Attempts to avoid such mistakes lead to a very slow, for some tasks unacceptably
slow, operation. Difficulties grow when three-dimensional motion and whole-
body collision avoidance are required. Operators are confused with simultaneous
choices—say, taking care of the arm’s end effector motion while avoiding colli-
sion at the arm’s elbow. Or, when moving a complex-shaped body in a crowded
space, especially if facing simultaneous potential collisions at different points of
the body, operators miss good options. It is known that losing a sense of direction
is detrimental to humans; for example, during deep dives the so-called Diver’s
Anxiety Syndrome interferes with the ability of professional divers to distinguish
up from down, leading to psychological stress and loss in performance.
Furthermore, training helps little: As discussed in much detail in Chapter 7,
humans are not particularly good in learning complex spatial reasoning tasks.
These problems, which tend to be explained away as artifacts of poor teleoper-
ation system design or insufficient training or inadequate input information, can
now be traced to the human’s inherent relatively poor ability for spatial reasoning.
We will learn in Chapter 7 that in some tasks that involve space reasoning,
robots can think better than humans. Note the emphasis: We are not saying that
robots can think faster or compute more accurately or memorize more data than
humans—we are saying that robots can think better under the same conditions.
This suggests a good potential for a synergism: In tasks that require exten-
sive spatial reasoning and where human and robot thinking/planning abilities are