  Avoiding obstacles in the perceived world has been the classical approach to
autonomous mobile robot navigation. However, this usually leads to unnatural
and inefficient motions that significantly differ from the way humans move in
tight and dynamic spaces, as we do not refrain interacting with the environment
around us when necessary. Inspired by this observation, we propose a framework
for autonomous robot navigation among movable obstacles (NAMO) that is based on
the theory of affordances and contact-implicit motion planning. We consider a
realistic scenario in which a mobile service robot negotiates unknown obstacles
in the environment while navigating to a goal state. An affordance extraction
procedure is performed for novel obstacles to detect their movability, and a
contact-implicit trajectory optimization method is used to enable the robot to
interact with movable obstacles to improve the task performance or to complete
an otherwise infeasible task. We demonstrate the performance of the proposed
framework by hardware experiments with Toyota's Human Support Robot.
