  Autonomously exploring the unknown physical properties of novel objects such
as stiffness, mass, center of mass, friction coefficient, and shape is crucial
for autonomous robotic systems operating continuously in unstructured
environments. We introduce a novel visuo-tactile based predictive cross-modal
perception framework where initial visual observations (shape) aid in obtaining
an initial prior over the object properties (mass). The initial prior improves
the efficiency of the object property estimation, which is autonomously
inferred via interactive non-prehensile pushing and using a dual filtering
approach. The inferred properties are then used to enhance the predictive
capability of the cross-modal function efficiently by using a human-inspired
`surprise' formulation. We evaluated our proposed framework in the real-robotic
scenario, demonstrating superior performance.
