  Multi-fingered hands could be used to achieve many dexterous manipulation
tasks, similarly to humans, and tactile sensing could enhance the manipulation
stability for a variety of objects. However, tactile sensors on multi-fingered
hands have a variety of sizes and shapes. Convolutional neural networks (CNN)
can be useful for processing tactile information, but the information from
multi-fingered hands needs an arbitrary pre-processing, as CNNs require a
rectangularly shaped input, which may lead to unstable results. Therefore, how
to process such complex shaped tactile information and utilize it for achieving
manipulation skills is still an open issue. This paper presents a control
method based on a graph convolutional network (GCN) which extracts geodesical
features from the tactile data with complicated sensor alignments. Moreover,
object property labels are provided to the GCN to adjust in-hand manipulation
motions. Distributed tri-axial tactile sensors are mounted on the fingertips,
finger phalanges and palm of an Allegro hand, resulting in 1152 tactile
measurements. Training data is collected with a data-glove to transfer human
dexterous manipulation directly to the robot hand. The GCN achieved high
success rates for in-hand manipulation. We also confirmed that fragile objects
were deformed less when correct object labels were provided to the GCN. When
visualizing the activation of the GCN with a PCA, we verified that the network
acquired geodesical features. Our method achieved stable manipulation even when
an experimenter pulled a grasped object and for untrained objects.
