  PointGoal Navigation is an embodied task that requires agents to navigate to
a specified point in an unseen environment. Wijmans et al. showed that this
task is solvable but their method is computationally prohibitive, requiring 2.5
billion frames and 180 GPU-days. In this work, we develop a method to
significantly increase sample and time efficiency in learning PointNav using
self-supervised auxiliary tasks (e.g. predicting the action taken between two
egocentric observations, predicting the distance between two observations from
a trajectory,etc.).We find that naively combining multiple auxiliary tasks
improves sample efficiency,but only provides marginal gains beyond a point. To
overcome this, we use attention to combine representations learnt from
individual auxiliary tasks. Our best agent is 5.5x faster to reach the
performance of the previous state-of-the-art, DD-PPO, at 40M frames, and
improves on DD-PPO's performance at 40M frames by 0.16 SPL. Our code is
publicly available at https://github.com/joel99/habitat-pointnav-aux.
