  In recent years, learning-based approaches have demonstrated significant
promise in addressing intricate navigation tasks. Traditional methods for
training deep neural network navigation policies rely on meticulously designed
reward functions or extensive teleoperation datasets as navigation
demonstrations. However, the former is often confined to simulated
environments, and the latter demands substantial human labor, making it a
time-consuming process. Our vision is for robots to autonomously learn
navigation skills and adapt their behaviors to environmental changes without
any human intervention. In this work, we discuss the self-supervised navigation
problem and present Dynamic Graph Memory (DGMem), which facilitates training
only with on-board observations. With the help of DGMem, agents can actively
explore their surroundings, autonomously acquiring a comprehensive navigation
policy in a data-efficient manner without external feedback. Our method is
evaluated in photorealistic 3D indoor scenes, and empirical studies demonstrate
the effectiveness of DGMem.
