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Teaching robots how to apply forces according to our preferences is still anopen challenge that has to be tackled from multiple engineering perspectives.This paper studies how to learn variable impedance policies where both theCartesian stiffness and the attractor can be learned from human demonstrationsand corrections with a user-friendly interface. The presented framework, namedILoSA, uses Gaussian Processes for policy learning, identifying regions ofuncertainty and allowing interactive corrections, stiffness modulation andactive disturbance rejection. The experimental evaluation of the framework iscarried out on a Franka-Emika Panda in four separate cases with unique forceinteraction properties: 1) pulling a plug wherein a sudden force discontinuityoccurs upon successful removal of the plug, 2) pushing a box where a sustainedforce is required to keep the robot in motion, 3) wiping a whiteboard in whichthe force is applied perpendicular to the direction of movement, and 4)inserting a plug to verify the usability for precision-critical tasks in anexperimental validation performed with non-expert users.

ILoSA: Interactive Learning of Stiffness and Attractors