Self-Supervised Learning Models for Autonomous Robotic Navigation

Abstract

Autonomous robotic navigation has become a key capability for intelligent robots operating in dynamic environments such as warehouses, hospitals, smart cities, agriculture, and autonomous transportation. While supervised learning methods achieve strong navigation performance, they depend on large labeled datasets that are costly and time-consuming to obtain. Self-Supervised Learning (SSL) addresses this limitation by enabling robots to learn robust visual and spatial representations directly from unlabeled sensor data through self-generated learning objectives. This paper reviews recent advances in SSL techniques, including representation learning, contrastive learning, predictive learning, masked image modeling, and multimodal sensor fusion for autonomous navigation. It also examines the integration of data from RGB cameras, LiDAR, IMUs, GPS, depth sensors, and odometry to improve perception, localization, obstacle avoidance, and path planning in unknown environments. Finally, the paper discusses key challenges such as domain adaptation, computational efficiency, safety, and continual learning, highlighting SSL's potential to enable scalable, adaptive, and lifelong autonomous robotic navigation.