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Handling non-rigid objects using robot hands necessities a framework thatdoes not only incorporate human-level dexterity and cognition but also themulti-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 andmanipulation, is augmented with visuo-tactile perception for autonomous andflexible adaptation to unknown objects. To detect objects and estimate theirposes, a simplified visual pipeline using RANSAC algorithm with Euclideanclustering and SVM classifier is exploited. To modulate interaction effortswhile grasping and manipulating non-rigid objects, the tactile feedback usingT40S shokac chip sensor, generating 3D force information, is incorporated.Moreover, different kernel functions are examined in the kernelized synergiesframework, to evaluate its performance and potential against taskreproducibility, execution, generalization and synergistic re-usability.Experiments performed with robot arm-hand system validates the capability andusability of upgraded framework on stably grasping and dexterously manipulatingthe non-rigid objects.

Fusing Visuo-Tactile Perception into Kernelized Synergies for Robust Grasping and Fine Manipulation of Non-rigid Objects