  In this paper we propose a novel method for in-hand object recognition. The
method is composed of a grasp stabilization controller and two exploratory
behaviours to capture the shape and the softness of an object. Grasp
stabilization plays an important role in recognizing objects. First, it
prevents the object from slipping and facilitates the exploration of the
object. Second, reaching a stable and repeatable position adds robustness to
the learning algorithm and increases invariance with respect to the way in
which the robot grasps the object. The stable poses are estimated using a
Gaussian mixture model (GMM). We present experimental results showing that
using our method the classifier can successfully distinguish 30 objects.We also
compare our method with a benchmark experiment, in which the grasp
stabilization is disabled. We show, with statistical significance, that our
method outperforms the benchmark method.
