  Humans in contrast to robots are excellent in performing fine manipulation
tasks owing to their remarkable dexterity and sensorimotor organization.
Enabling robots to acquire such capabilities, necessitates a framework that not
only replicates the human behaviour but also integrates the multi-sensory
information for autonomous object interaction. To address such limitations,
this research proposes to augment the previously developed kernelized synergies
framework with visual perception to automatically adapt to the unknown objects.
The kernelized synergies, inspired from humans, retain the same reduced
subspace for object grasping and manipulation. To detect object in the scene, a
simplified perception pipeline is used that leverages the RANSAC algorithm with
Euclidean clustering and SVM for object segmentation and recognition
respectively. Further, the comparative analysis of kernelized synergies with
other state of art approaches is made to confirm their flexibility and
effectiveness on the robotic manipulation tasks. The experiments conducted on
the robot hand confirm the robustness of modified kernelized synergies
framework against the uncertainties related to the perception of environment.
