  The U.S. Defense Advanced Research Projects Agency (DARPA) Subterranean
Challenge requires teams of robots to traverse difficult and diverse
underground environments. Traversing small gaps is one of the challenging
scenarios that robots encounter. Imperfect sensor information makes it
difficult for classical navigation methods, where behaviours require
significant manual fine tuning. In this paper we present a deep reinforcement
learning method for autonomously navigating through small gaps, where contact
between the robot and the gap may be required. We first learn a gap behaviour
policy to get through small gaps (only centimeters wider than the robot). We
then learn a goal-conditioned behaviour selection policy that determines when
to activate the gap behaviour policy. We train our policies in simulation and
demonstrate their effectiveness with a large tracked robot in simulation and on
the real platform. In simulation experiments, our approach achieves 93\%
success rate when the gap behaviour is activated manually by an operator, and
63\% with autonomous activation using the behaviour selection policy. In real
robot experiments, our approach achieves a success rate of 73\% with manual
activation, and 40\% with autonomous behaviour selection. While we show the
feasibility of our approach in simulation, the difference in performance
between simulated and real world scenarios highlight the difficulty of direct
sim-to-real transfer for deep reinforcement learning policies. In both the
simulated and real world environments alternative methods were unable to
traverse the gap.

Robotics;Artificial Intelligence