Autonomous robots would benefit a lot by gaining the ability to manipulate
their environment to solve path planning tasks, known as the Navigation Among
Movable Obstacle (NAMO) problem. In this paper, we present a deep reinforcement
learning approach for solving NAMO locally, near narrow passages. We train
parallel agents in physics simulation using an Advantage Actor-Critic based
algorithm with a multi-modal neural network. We present an online policy that
is able to push obstacles in a non-axial-aligned fashion, react to unexpected
obstacle dynamics in real-time, and solve the local NAMO problem. Experimental
validation in simulation shows that the presented approach generalises to
unseen NAMO problems in unknown environments. We further demonstrate the
implementation of the policy on a real quadrupedal robot, showing that the
policy can deal with real-world sensor noises and uncertainties in unseen NAMO
tasks.Comment: 7 pages, 7 figures, 4 table