  The object perception capabilities of humans are impressive, and this becomes
even more evident when trying to develop solutions with a similar proficiency
in autonomous robots. While there have been notable advancements in the
technologies for artificial vision and touch, the effective integration of
these two sensory modalities in robotic applications still needs to be
improved, and several open challenges exist. Taking inspiration from how humans
combine visual and haptic perception to perceive object properties and drive
the execution of manual tasks, this article summarises the current state of the
art of visuo-haptic object perception in robots. Firstly, the biological basis
of human multimodal object perception is outlined. Then, the latest advances in
sensing technologies and data collection strategies for robots are discussed.
Next, an overview of the main computational techniques is presented,
highlighting the main challenges of multimodal machine learning and presenting
a few representative articles in the areas of robotic object recognition,
peripersonal space representation and manipulation. Finally, informed by the
latest advancements and open challenges, this article outlines promising new
research directions.
