  Sim-to-real transfer is a powerful paradigm for robotic reinforcement
learning. The ability to train policies in simulation enables safe exploration
and large-scale data collection quickly at low cost. However, prior works in
sim-to-real transfer of robotic policies typically do not involve any
human-robot interaction because accurately simulating human behavior is an open
problem. In this work, our goal is to leverage the power of simulation to train
robotic policies that are proficient at interacting with humans upon
deployment. But there is a chicken and egg problem -- how to gather examples of
a human interacting with a physical robot so as to model human behavior in
simulation without already having a robot that is able to interact with a
human? Our proposed method, Iterative-Sim-to-Real (i-S2R), attempts to address
this. i-S2R bootstraps from a simple model of human behavior and alternates
between training in simulation and deploying in the real world. In each
iteration, both the human behavior model and the policy are refined. For all
training we apply a new evolutionary search algorithm called Blackbox Gradient
Sensing (BGS). We evaluate our method on a real world robotic table tennis
setting, where the objective for the robot is to play cooperatively with a
human player for as long as possible. Table tennis is a high-speed, dynamic
task that requires the two players to react quickly to each other's moves,
making for a challenging test bed for research on human-robot interaction. We
present results on an industrial robotic arm that is able to cooperatively play
table tennis with human players, achieving rallies of 22 successive hits on
average and 150 at best. Further, for 80% of players, rally lengths are 70% to
175% longer compared to the sim-to-real plus fine-tuning (S2R+FT) baseline. For
videos of our system in action, please see https://sites.google.com/view/is2r.
