Chapter 2. Prehension

132 THE PHASES OF PREHENSION
MODEL
NETWORK
NETWORK
Figure 5.8. Jordan’s network for computing inverse kinematics of
a sequence of actions. It consists of two networks. The upper one,
the Model Network, computes the forward kinematics for the
manipulator. The lower one, the Sequential Network, computes the
trajectory in joint space that will bring the endpoint of the
manipulator to the desired hand space locations. In the inset, the
six degree of freedom manipulator is seen, consisting of two
translational degrees of freedom and four rotational ones (from
Jordan, 1988; adapted by permission).
reduce this difference. Computing the forward kinematics in this
manner took about 2000 times of repeated trials, converging on a
solution when the difference, or error, between the desired and actual
endpoint was reduced to almost zero.
During the second phase, the sequence of kinematic configurations
is learned using the Sequential Network (bottom half of the figure).
The state units are initialized to a time step of 0. A plan (a sequence of
goal locations) is presented to the plan units. For a given time step, a
joint configuration is computed in a method similar to that described