  The common approach for local navigation on challenging environments with
legged robots requires path planning, path following and locomotion, which
usually requires a locomotion control policy that accurately tracks a commanded
velocity. However, by breaking down the navigation problem into these
sub-tasks, we limit the robot's capabilities since the individual tasks do not
consider the full solution space. In this work, we propose to solve the
complete problem by training an end-to-end policy with deep reinforcement
learning. Instead of continuously tracking a precomputed path, the robot needs
to reach a target position within a provided time. The task's success is only
evaluated at the end of an episode, meaning that the policy does not need to
reach the target as fast as possible. It is free to select its path and the
locomotion gait. Training a policy in this way opens up a larger set of
possible solutions, which allows the robot to learn more complex behaviors. We
compare our approach to velocity tracking and additionally show that the time
dependence of the task reward is critical to successfully learn these new
behaviors. Finally, we demonstrate the successful deployment of policies on a
real quadrupedal robot. The robot is able to cross challenging terrains, which
were not possible previously, while using a more energy-efficient gait and
achieving a higher success rate.
