  Robotic interaction in fast-paced environments presents a substantial
challenge, particularly in tasks requiring the prediction of dynamic,
non-stationary objects for timely and accurate responses. An example of such a
task is ping-pong, where the physical limitations of a robot may prevent it
from reaching its goal in the time it takes the ball to cross the table. The
scene of a ping-pong match contains rich visual information of a player's
movement that can allow future game state prediction, with varying degrees of
uncertainty. To this aim, we present a visual modeling, prediction, and control
system to inform a ping-pong playing robot utilizing visual model uncertainty
to allow earlier motion of the robot throughout the game. We present
demonstrations and metrics in simulation to show the benefit of incorporating
model uncertainty, the limitations of current standard model uncertainty
estimators, and the need for more verifiable model uncertainty estimation. Our
code is publicly available.
