Many people face mobility challenges due to spinal cord injury, stroke, and aging. Therapeutic interventions using assistive exoskeletons have emerged as promising tools to enhance their quality of life. The efficacy of exoskeletons requires a delicate balance between assistance and allowing users to regain control of their movements. This needs the exoskeleton to continuously alternate between follower and leader roles to assist the user only when needed. My PhD research focused on proposing a solution for this challenge by optimizing the human-exoskeleton physical interaction. I developed an innovative method to optimize interaction torques, enabling the exoskeleton to adapt its assistance based on the user's motor capacity. Using musculoskeletal modelling and simulation tools such as OpenSim and MATLAB, I integrated human and exoskeleton models and simulated individuals with varying levels of injuries. I implemented an adaptive approach to determine an efficient exoskeleton trajectory, resulting in improved gait stability and spatiotemporal parameters by decreasing the physical disagreement between the user and the exoskeleton, which is expected to increase the user comfort level. I further extended the optimization formulation to adapt to the changes in gait speed, transitions, and pathological gait patterns by developing a data-driven gait phase estimator using a rich dataset collected from 14 participants, offering superior performance in estimating gait phases under diverse conditions. Moreover, I tackled the issue of measuring interaction torques in practice, where direct measurements are impractical due to the complex nature of human-exoskeleton interaction. I introduced an innovative excitation approach to capture the dynamics of the exoskeleton in all regimes (i.e., swing, stance, and double support) with a single model. This method allows researchers to estimate interaction torques throughout the entire gait phase, ensuring accurate monitoring of the human-exoskeleton interaction dynamics. Leveraging these contributions, I implemented my optimization method in practical settings, and validated its effectiveness in experiments on 15 participants during treadmill and overground walking. Finally, I developed an adaptive feedforward torque controller capable of learning the user desired joint trajectory and accordingly generating an appropriate feedforward torque based on the exoskeleton dynamical model. Comparative assessments on 9 participants against current methods demonstrated that my controller reduces metabolic costs, physical interaction, and enhances the overall user experience compared to a recently developed stated-dependent feedforward controller. As a part of this assessment, I proposed a new method of evaluating human-exoskeleton interaction based on co-analysis of the user muscular effort and the interaction torques called Interaction Portrait. I showed that the distribution of the interaction portrait can determine different regimes of human-exoskeleton physical interaction. In conclusion, the methodologies I introduced contributed to the advancement of assistive robotics. By focusing on optimizing interaction torques, I addressed a key limitation in contemporary exoskeleton designs, ensuring the device intelligently adapts to the user's unique motor capacities. By successfully addressing real-world challenges, such as adapting to diverse gait patterns and accurately estimating interaction torques, my research offers a tangible and significant improvement in exoskeleton performance. The practical implementation and subsequent evaluations underscore the potential of my approach to not only enhance mobility but also elevate the user experience. My research lays a strong foundation for future endeavours aimed at bridging the gap between robotic assistance and human motor impairments.