  In this paper, we propose a leader-follower hierarchical strategy for two
robots collaboratively transporting an object in a partially known environment
with obstacles. Both robots sense the local surrounding environment and react
to obstacles in their proximity. We consider no explicit communication, so the
local environment information and the control actions are not shared between
the robots. At any given time step, the leader solves a model predictive
control (MPC) problem with its known set of obstacles and plans a feasible
trajectory to complete the task. The follower estimates the inputs of the
leader and uses a policy to assist the leader while reacting to obstacles in
its proximity. The leader infers obstacles in the follower's vicinity by using
the difference between the predicted and the real-time estimated follower
control action. A method to switch the leader-follower roles is used to improve
the control performance in tight environments. The efficacy of our approach is
demonstrated with detailed comparisons to two alternative strategies, where it
achieves the highest success rate, while completing the task fastest.
