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Manipulation in contrast to grasping is a trajectorial task that needs to usedexterous hands. Improving the dexterity of robot hands, increases thecontroller complexity and thus requires to use the concept of posturalsynergies. Inspired from postural synergies, this research proposes a newframework called kernelized synergies that focuses on the re-usability of thesame subspace for precision grasping and dexterous manipulation. In this work,the computed subspace of postural synergies; parameterized by probabilisticmovement primitives, is treated with kernel to preserve its grasping andmanipulation characteristics and allows its reuse for new objects. The graspstability of the proposed framework is assessed with a force closure qualityindex. For performance evaluation, the proposed framework is tested on twodifferent simulated robot hand models using the Syngrasp toolbox andexperimentally, four complex grasping and manipulation tasks are performed andreported. The results confirm the hand agnostic approach of the proposedframework and its generalization to distinct objects irrespective of theirshape and size.

Leveraging Kernelized Synergies on Shared Subspace for Precision Grasp and Dexterous Manipulation