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Chapter 4
Human-machine Cooperative Manipulation
with Vision-based Motion Constraints
Gregory D. Hager
Abstract. This chapter discusses a class of control algorithms that provide enhanced
physical dexterity by imposing passive motion constraints. Such motion constraints
are often referred to as virtual fixtures. It is shown that algorithms originally de-
signed for vision-based control of manipulators can be easily converted into control
algorithms that provide virtual fixtures. As a result it is possible to create advanced
human-machine cooperative manipulation systems that take complete advantage of
information provided by vision, yet permit the user to retain control of essential
aspects of a given task.
4.1
Introduction
Much of “classical” robotics has focused on creating machines that are autonomous.
However, such endeavors are fundamentally limited by our ability to create ma-
chines that can perceive, judge, and react to unforeseen (or sometimes foreseen!)
circumstances in the world. To this day, there are still few situations, other than rote,
open-loop manipulation, where robotics have even begun to compete with humans,
in this regard.
At the other extreme, teleoperation tends to focus on providing a high-performance,
high-fidelity operator interface to a machine. In many ways, this is a natural marriage,
as the human now controls a machine that may be more accurate, reliable, or power-
ful than the human operator. However, while the machine is clearly amplifying some
level of human skill, it does so at the cost of attenuating others. In particular, teleop-
eration systems do not have an underlying representation of the intent of the operator.
As a result, it is not possible to adapt or modify the interface to enhance those skills
that are most germaine to the task at hand.
Our group in the center for computer integrated surgical systems (CISST) has
been working to create surgical systems that improve both the speed and precision
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