A system for rehabilitation and/or mobility of a user, comprising: a frame, comprising a lower body and upper body; wheels, attached to the frame for rolling movement; a pelvis support, attached to the frame for distributing at least partial weight of the user to the frame; and a motorized unit, operably attached to the frame for assisting movement of the user, comprising at least one actuator, and an attachment for attaching to a part of a lower body of the user, wherein the system further comprises an artificial intelligence based control system for controlling the motorized unit, the artificial intelligence based control system comprising at least one processor and memory, characterized in that the artificial intelligence based control system comprises: an artificial intelligence based settings recommender engine for using learning functions to output improved recommendations for gait settings based on a score assigned to gait settings by the user via a user interface, wherein the score represents the gait settings' applicability over time and across different mobile systems, the gait settings determining a plurality of gait trajectories to apply to the motorized unit, an artificial intelligence based control engine which maps each motorized unit's joint data corresponding to position, velocity and torque and user profile data to a gait action such as turning through a sequence learning model such as a recurrent neural network model including a hidden layer which converts all motorized unit's joint data into different gait phases, and an artificial intelligence based long-term learning system which combines different mobile systems' sequence learning models to create a deep neural network for gait actions across different disabilities and users. a frame, comprising a lower body and upper body; wheels, attached to the frame for rolling movement; a pelvis support, attached to the frame for distributing at least partial weight of the user to the frame; and a motorized unit, operably attached to the frame for assisting movement of the user, comprising at least one actuator, and an attachment for attaching to a part of a lower body of the user, wherein the system further comprises an artificial intelligence based control system for controlling the motorized unit, the artificial intelligence based control system comprising at least one processor and memory, characterized in that the artificial intelligence based control system comprises: an artificial intelligence based settings recommender engine for using learning functions to output improved recommendations for gait settings based on a score assigned to gait settings by the user via a user interface, wherein the score represents the gait settings' applicability over time and across different mobile systems, the gait settings determining a plurality of gait trajectories to apply to the motorized unit, an artificial intelligence based control engine which maps each motorized unit's joint data corresponding to position, velocity and torque and user profile data to a gait action such as turning through a sequence learning model such as a recurrent neural network model including a hidden layer which converts all motorized unit's joint data into different gait phases, and an artificial intelligence based long-term learning system which combines different mobile systems' sequence learning models to create a deep neural network for gait actions across different disabilities and users. an artificial intelligence based settings recommender engine for using learning functions to output improved recommendations for gait settings based on a score assigned to gait settings by the user via a user interface, wherein the score represents the gait settings' applicability over time and across different mobile systems, the gait settings determining a plurality of gait trajectories to apply to the motorized unit, an artificial intelligence based control engine which maps each motorized unit's joint data corresponding to position, velocity and torque and user profile data to a gait action such as turning through a sequence learning model such as a recurrent neural network model including a hidden layer which converts all motorized unit's joint data into different gait phases, and an artificial intelligence based long-term learning system which combines different mobile systems' sequence learning models to create a deep neural network for gait actions across different disabilities and users.