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.

翻译：近年来，基于学习的方法在解决复杂导航任务中展现出显著潜力。传统深度神经网络导航策略的训练依赖于精心设计的奖励函数或大规模遥操作数据集作为导航示范。然而，前者通常局限于仿真环境，后者则需要大量人力投入，是一项耗时的工作。我们的愿景是让机器人能够自主习得导航技能，并在无需任何人工干预的情况下适应环境变化。本文探讨了自监督导航问题，并提出动态图记忆（Dynamic Graph Memory, DGMem），该机制仅利用车载观测数据即可完成训练。借助DGMem，智能体能够主动探索周围环境，以数据高效的方式自主获取全面的导航策略，无需外部反馈。我们在逼真三维室内场景中评估了该方法，实证研究证明了DGMem的有效性。