  Adaptive control for real-time manipulation requires quick estimation and
prediction of object properties. While robot learning in this area primarily
focuses on using vision, many tasks cannot rely on vision due to object
occlusion. Here, we formulate a learning framework that uses multimodal sensory
fusion of tactile and audio data in order to quickly characterize and predict
an object's properties. The predictions are used in a developed reactive
controller to adapt the grip on the object to compensate for the predicted
inertial forces experienced during motion. Drawing inspiration from how humans
interact with objects, we propose an experimental setup from which we can
understand how to best utilize different sensory signals and actively interact
with and manipulate objects to quickly learn their object properties for safe
manipulation.
