  Grip control during robotic in-hand manipulation is usually modeled as part
of a monolithic task, relying on complex controllers specialized for specific
situations. Such approaches do not generalize well and are difficult to apply
to novel manipulation tasks. Here, we propose a modular object stabilization
method based on a proposition that explains how humans achieve grasp stability.
In this bio-mimetic approach, independent tactile grip stabilization
controllers ensure that slip does not occur locally at the engaged robot
fingers. Such local slip is predicted from the tactile signals of each
fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable
grasps emerge without any form of central communication when such independent
controllers are engaged in the control of multi-digit robotic hands. These
grasps are resistant to external perturbations while being capable of
stabilizing a large variety of objects.
