  In this paper, we present a planning framework that uses a combination of
implicit (robot motion) and explicit (visual/audio/haptic feedback)
communication during mobile robot navigation. First, we developed a model that
approximates both continuous movements and discrete behavior modes in human
navigation, considering the effects of implicit and explicit communication on
human decision making. The model approximates the human as an optimal agent,
with a reward function obtained through inverse reinforcement learning. Second,
a planner uses this model to generate communicative actions that maximize the
robot's transparency and efficiency. We implemented the planner on a mobile
robot, using a wearable haptic device for explicit communication. In a user
study of an indoor human-robot pair of orthogonal crossing situation, the robot
was able to actively communicate its intent to users in order to avoid
collisions and facilitate efficient trajectories. Results showed that the
planner generated plans that were easier to understand, reduced users' effort,
and increased users' trust of the robot, compared to simply performing
collision avoidance. The key contribution of this work is the integration and
analysis of explicit communication (together with implicit communication) for
social navigation.
