Research

PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment. Wijmans et al. [1] showed that this task is solvable in simulation but their method is computationally prohibitive – requiring 2.5 billion frames of experience and 180 GPU-days. We develop a method to significantly improve sample 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 from individual auxiliary tasks. Our best agent is 5.5x faster to match the performance of the previous state-of-the-art, DD-PPO [1], 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.