  We present a robot-to-human object handover algorithm and implement it on a
7-DOF arm equipped with a 3-finger mechanical hand. The system performs a fully
autonomous and robust object handover to a human receiver in real-time. Our
algorithm relies on two complementary sensor modalities: joint torque sensors
on the arm and an eye-in-hand RGB-D camera for sensor feedback. Our approach is
entirely implicit, i.e., there is no explicit communication between the robot
and the human receiver. Information obtained via the aforementioned sensor
modalities is used as inputs to their related deep neural networks. While the
torque sensor network detects the human receiver's "intention" such as: pull,
hold, or bump, the vision sensor network detects if the receiver's fingers have
wrapped around the object. Networks' outputs are then fused, based on which a
decision is made to either release the object or not. Despite substantive
challenges in sensor feedback synchronization, object, and human hand
detection, our system achieves robust robot-to-human handover with 98\%
accuracy in our preliminary real experiments using human receivers.
