  Recent advances in the field of intelligent robotic manipulation pursue
providing robotic hands with touch sensitivity. Haptic perception encompasses
the sensing modalities encountered in the sense of touch (e.g., tactile and
kinesthetic sensations). This letter focuses on multimodal object recognition
and proposes analytical and data-driven methodologies to fuse tactile- and
kinesthetic-based classification results. The procedure is as follows: a
three-finger actuated gripper with an integrated high-resolution tactile sensor
performs squeeze-and-release Exploratory Procedures (EPs). The tactile images
and kinesthetic information acquired using angular sensors on the finger joints
constitute the time-series datasets of interest. Each temporal dataset is fed
to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify
in-hand objects. The LSTMs provide an estimation of the posterior probability
of each object given the corresponding measurements, which after fusion allows
to estimate the object through Bayesian and Neural inference approaches. An
experiment with 36-classes is carried out to evaluate and compare the
performance of the fused, tactile, and kinesthetic perception systems.The
results show that the Bayesian-based classifiers improves capabilities for
object recognition and outperforms the Neural-based approach.
