Chapter 2. Prehension

A
Chapter 4 - Planning of Prehension 85
Chosen B Chosen
Opposition
R
A Opposition
Surface Object Amount Task
length width force mecision
Figure 4.8 Choosing an opposition space from task and object
properties using neural networks. A. Network architecture showing
four input units, four hidden units, and one output unit. B.
Weights between network elements. Black squares are negative
weights, white squares are positive weights. The size of the square
is proportional to the magnitude of the weight. Grey is threshold.
The leftmost column shows weights from the surface length input
to the hidden layer, etc. The topmost row represents the weights
from the hidden layer to the output unit (from Iberall, 1988).
mapping forces the modeller to be more careful about defining terms,
and makes explicit just what the information is that is required in order
to deduce the appropriate grasp. In addition, 'what-if' questions can
be asked, and patterns or sequences among grasps can be explored.
An alternative approach for choosing oppositions is to use a neural
network (See Appendix C for a more detailed explanation of artificial
neural networks). In contrast to expert systems, the inputs (object and
task requirements) and outputs (oppositions) are characterized but the
network learns the mapping rules without their being ma& explicit.
Iberall(l988) used a simulated neural network to chose an opposition
for a given set of task requirements. As seen on the left of Figure 4.8,
salient task features included two perceived intrinsic object properties