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Characterization of textile grasping experiments

Grasping highly deformable objects, like textiles, 
is an emerging area of research that involves both percep- 
tion and manipulation abilities. As new techniques appear, 
it becomes essential to design strategies to compare them. 
However, this is not an easy task, since the large state-space 
of textile objects explodes when coupled with the variability 
of grippers, robotic hands and robot arms performing the 
manipulation task. This high variability makes it very difficult 
to design experiments to evaluate the performance of a system 
in a repeatable way and compare it to others. We propose 
a framework to allow the comparison of different grasping 
methods for textile objects. 
Instead of measuring each component separately, we there- 
fore propose a methodology to explicitly measure the vision- 
manipulation correlation by taking into account the throughput 
of the actions. Perceptions of deformable objects should be 
grouped into different clusters, and the different grasping 
actions available should be tested for each perception type to 
obtain the action-perception success ratio. This characterization 
potentially allows to compare very different systems in terms 
of 
specialized actions 
, 
perceptions 
or 
widely useful actions 
, along 
with the cost of performing each action. We will also show 
that this categorization is useful in manipulation planning of 
deformable objects.