  This work contributes to the development of active haptic exploration
strategies of surfaces using robotic hands in environments with an unknown
structure. The architecture of the proposed approach consists two main Bayesian
models, implementing the touch attention mechanisms of the system. The model
pi_per perceives and discriminates different categories of materials (haptic
stimulus) integrating compliance and texture features extracted from haptic
sensory data. The model pi_tar actively infers the next region of the workspace
that should be explored by the robotic system, integrating the task
information, the permanently updated saliency and uncertainty maps extracted
from the perceived haptic stimulus map, as well as, inhibition-of-return
mechanisms.
  The experimental results demonstrate that the Bayesian model pi_per can be
used to discriminate 10 different classes of materials with an average
recognition rate higher than 90% . The generalization capability of the
proposed models was demonstrated experimentally. The ATLAS robot, in the
simulation, was able to perform the following of a discontinuity between two
regions made of different materials with a divergence smaller than 1cm (30
trials). The tests were performed in scenarios with 3 different configurations
of the discontinuity. The Bayesian models have demonstrated the capability to
manage the uncertainty about the structure of the surfaces and sensory noise to
make correct motor decisions from haptic percepts.
