  Manipulation in contrast to grasping is a trajectorial task that needs to use
dexterous hands. Improving the dexterity of robot hands, increases the
controller complexity and thus requires to use the concept of postural
synergies. Inspired from postural synergies, this research proposes a new
framework called kernelized synergies that focuses on the re-usability of the
same subspace for precision grasping and dexterous manipulation. In this work,
the computed subspace of postural synergies; parameterized by probabilistic
movement primitives, is treated with kernel to preserve its grasping and
manipulation characteristics and allows its reuse for new objects. The grasp
stability of the proposed framework is assessed with a force closure quality
index. For performance evaluation, the proposed framework is tested on two
different simulated robot hand models using the Syngrasp toolbox and
experimentally, four complex grasping and manipulation tasks are performed and
reported. The results confirm the hand agnostic approach of the proposed
framework and its generalization to distinct objects irrespective of their
shape and size.
