  Communication is a hallmark of intelligence. In this work, we present MIRROR,
an approach to (i) quickly learn human models from human demonstrations, and
(ii) use the models for subsequent communication planning in assistive
shared-control settings. MIRROR is inspired by social projection theory, which
hypothesizes that humans use self-models to understand others. Likewise, MIRROR
leverages self-models learned using reinforcement learning to bootstrap human
modeling. Experiments with simulated humans show that this approach leads to
rapid learning and more robust models compared to existing behavioral cloning
and state-of-the-art imitation learning methods. We also present a
human-subject study using the CARLA simulator which shows that (i) MIRROR is
able to scale to complex domains with high-dimensional observations and
complicated world physics and (ii) provides effective assistive communication
that enabled participants to drive more safely in adverse weather conditions.
