For some dexterity robotic tasks, such as turning a constrained dynamic object, rolling motion is a crucial advanced characteristic, which for the human hand is possible from the shape and soft tip in our fingers. This paper presents the manipulation of a rigid constrained dynamic circular object using a robotic finger equipped with a hemispherical and soft tip. Assuming that object angle measurement is not available, a force-position regulator is proposed to guarantee the control of contact force, as well as the object angle throughout the tangential force. To establish the relationship between the rolling angle and the tangential force, the use of Echo State Network (ESN) becomes instrumental. A novel smooth weight adaptation scheme steered by a sliding mode attractor, for readout network is proposed. Stability conditions are obtained to guarantee convergence in finite time using small feedback gains and piecewise continuous functions. Additionally, the local asymptotic stability of the pair robot-object is guaranteed without force sensing, not either any knowledge of the deformation nor penetration measurement, leading surprisingly to a simple blind touch-type controller for such difficult task. Finally, numerical simulations show the learning rule contribution and the behavior of the closed-loop system.
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