Chapter 2. Prehension

Chapter 4 - Planning of Prehension 91
For intermediate objects, there were preferred grip patterns of two or
three fingers in opposition to the thumb. Further, the frequency
curves and patterns of hand use were similar for adults and children
when plotted against the objecthand ratio.
Iberall, Preti, and Zemke (1989) asked subjects to place cylinders
of various lengths (8 cm in diameter) on a platform using pad opposi-
tion. No instructions were given on how many fingers to use in VF2
as it opposed the thumb (VF1). Of the fifteen finger combinations
possible, seven combinations were used (index, middle, index &
middle, middle & ring, index 8z middle & ring, middle 8z ring 8z little,
index & middle 8z ring 8z little). The size of VF2 was 1,2, 3, or 4
fingers wide, although 60% of the grasps used a VF2 of 1 or 2
fingers. It was observed that more fingers were used in VF2 as
cylinder length increased, supporting Newel1 et al. (1989).
Surprisingly, of the VF2 with a width of one, subjects tended to use
their middle finger (M). In terms of finger occurrence, use of the
middle finger (M) was seen almost all the time, particularly since six
of the seven postures include the middle finger. Both the index finger
(I) and ring finger (R) increased in usage as cylinder length increased.
The little finger Q was brought in for largest cylinder.
How might virtual finger planning be modelled using artificial neu-
ral networks? Iberall, Preti, and Zemke (1989) constructed a network
to determine a real finger mapping for virtual figer two (VF2) in pad
opposition (Figure 4.11). The neural network learned how to assign
virtual to real finger mappings given length as the object characteristic
and difficulty as the task requirement. Supervised learning was used
to train the network. The training set was constructed using data from
the experiment described above. Training pairs were presented to the
network in thousands of trials until it learned to assign the correct
mapping of virtual to real fingers given these inputs.
Using the same supervised learning algorithm as previously de-
scribed, different tasks (task difficulty, cylinder length) were pre-
sented to the input layer. The number of real fingers to use was com-
puted by summing up weighted activation values of the input units and
then weighted activation values on the hidden layer. Iberall et al. used
the generalized delta rule to change the weights between the input
units, hidden layer, and output units. An error cutoff of 0.05 was
used to indicate that the network learned the training set, and had con-
verged on a solution.
Different architectures for adaptive neural networks were de-
signed. A network architecture that decides the average number of
fingers to use in virtual finger two (not shown), and percentages