  Handling non-rigid objects using robot hands necessities a framework that
does not only incorporate human-level dexterity and cognition but also the
multi-sensory information and system dynamics for robust and fine interactions.
In this research, our previously developed kernelized synergies framework,
inspired from human behaviour on reusing same subspace for grasping and
manipulation, is augmented with visuo-tactile perception for autonomous and
flexible adaptation to unknown objects. To detect objects and estimate their
poses, a simplified visual pipeline using RANSAC algorithm with Euclidean
clustering and SVM classifier is exploited. To modulate interaction efforts
while grasping and manipulating non-rigid objects, the tactile feedback using
T40S shokac chip sensor, generating 3D force information, is incorporated.
Moreover, different kernel functions are examined in the kernelized synergies
framework, to evaluate its performance and potential against task
reproducibility, execution, generalization and synergistic re-usability.
Experiments performed with robot arm-hand system validates the capability and
usability of upgraded framework on stably grasping and dexterously manipulating
the non-rigid objects.
