Autonomous mobile robots are beginning to try to help us provide different delivery services in people's lives, such as delivering medicines in hospitals, delivering goods in warehouses, and delivering food in restaurants. To realize this vision, robots need to navigate autonomously and efficiently through complex, crowded, and dynamic environments filled with static obstacles, such as tables and chairs, as well as people and/or other robots, and to achieve this using the computational resources available onboard a mobile robot. This dissertation improves the state-of-the-art in autonomous navigation by developing learning-based algorithms to model the environment around the robot, predict changes in the environment, and control the robot, all of which can run onboard a mobile robot in real time. Specifically, this dissertation first proposes a set of specialized preprocessed data representations to extract and encode useful high-level information about crowded dynamic environments from raw sensor data (i.e., a short history of lidar data, kinematic data about nearby pedestrians, and a sub-goal that leads the robots towards its final destination). Then, using these combined preprocessed data representations, this dissertation proposes a novel crowd-aware navigation control policy that can balance collision avoidance and speed in crowded dynamic scenes by designing a velocity obstacle-based reward function that is used to train the robot leveraging deep reinforcement learning techniques. This dissertation then proposes a series of hardware-friendly prediction algorithms, based on variational autoencoder networks, to predict a distribution of possible future states in dynamic scenes by exploiting the kinematics and dynamics of the robot and its surrounding objects. Furthermore, this dissertation proposes a novel predictive uncertainty-aware navigation framework to improve the safety performance of current existing control policies by incorporating the output of the proposed stochastic environment prediction algorithms into general navigation frameworks. Many different collected real-world datasets as well as a series of 3D simulation experiments and hardware experiments are used to demonstrate the effectiveness of these proposed novel learning-based prediction and control algorithms. The new algorithms outperform other state-of-the-art algorithms in terms of collision avoidance, robot speed, and prediction accuracy across a range of environments, crowd densities, and robot models. It is believed that all the work included in this dissertation will promote the development of autonomous navigation for modern mobile robots, provide highly innovative solutions to the open problem of autonomous navigation in crowded dynamic scenes, and make our daily lives more convenient and efficient. / Mechanical Engineering