  Object navigation is defined as navigating to an object of a given label in a
complex, unexplored environment. In its general form, this problem poses
several challenges for Robotics: semantic exploration of unknown environments
in search of an object and low-level control. In this work we study
object-guided exploration and low-level control, and present an end-to-end
trained navigation policy achieving a success rate of 0.68 and SPL of 0.58 on
unseen, visually complex scans of real homes. We propose a highly scalable
implementation of an off-policy Reinforcement Learning algorithm, distributed
Soft Actor Critic, which allows the system to utilize 98M experience steps in
24 hours on 8 GPUs. Our system learns to control a differential drive mobile
base in simulation from a stack of high dimensional observations commonly used
on robotic platforms. The learned policy is capable of object-guided
exploratory behaviors and low-level control learned from pure experiences in
realistic environments.
