Chapter 2. Prehension

Chapter 4 - Planning of Prehension 87
Table 4.1 Training set for neural network. A task is defined by
task requirements, which are stated in terms relative to a hand and
its CaDabilitv. For the given task reauirements. either Dad or Dalm
opposition & selected (from Iberall, i988; adapted by -permission).
TASK REQUIREMENTS CHOSEN
TASK Surface Object Size Precision OPPOS-
Length Width of
Forces
Needed ITION
Lift heavy
beer mug
>4 fingers large large low PALM
Lift long steel >4 fingers
cylinder
medium large low PALM
Lift short
cylinder
3 fingers medium large low PALM
Place wide 2fingers large medium high PAD
short steel
cylinder
Lift large >4fingers large medium medium PAD
glass
Place cylinder > 4 fingers medium small high PAD
Lift small lfinger small small medium PAD
disk
Lift med. disk 1 finger medium small low PAD
Place med. 1 finger medium small high PAD
disk
Throw med. 1 finger medium medium low PAD
disk
computed ouput, a logistic activation function was used. A
momentum term was added in order to increase the learning rate. An
error cutoff of 0.05 was used to indicate that the network learned the
training set. It took 833 repetitions of the training data to converge on
a solution. The weights into which the network settled are seen in
Figure 4.8b. The first row of squares from the bottom represent the
weights on the links from the input units to the first hidden neuron
(hidden neuron on the left in Figure 4.8a). The size of the square is
proportional to the magnitude of the weight. Larger squares mean a
larger influence. A negative influence is shown by black squares,
positive by white. The second row of squares from the bottom
represent the weights to the second hidden neuron, and so on. As can
be seen, the size of the squares in the third row from the bottom are
close to zero and thus the third hidden unit from the left has little