  We claim that navigation in human environments can be viewed as cooperative
activity especially in constrained situations. Humans concurrently aid and
comply with each other while moving in a shared space. Cooperation helps
pedestrians to efficiently reach their own goals and respect conventions such
as the personal space of others. To meet human comparable efficiency, a robot
needs to predict the human trajectories and plan its own trajectory
correspondingly in the same shared space. In this work, we present a navigation
planner that is able to plan such cooperative trajectories, simultaneously
enforcing the robot's kinematic constraints and avoiding other non-human
dynamic obstacles. Using robust social constraints of projected time to a
possible future collision, compatibility of human-robot motion direction, and
proxemics, our planner is able to replicate human-like navigation behavior not
only in open spaces but also in confined areas. Besides adapting the robot
trajectory, the planner is also able to proactively propose co-navigation
solutions by jointly computing human and robot trajectories within the same
optimization framework. We demonstrate richness and performance of the
cooperative planner with simulated and real world experiments on multiple
interactive navigation scenarios.
