In application domains such as surgical robotics, fully autonomous control remains a long-term ambition and the systems are mostly teleoperated. In this article, the presence of an operator in-the-loop is exploited to perform the online registration of an initially inaccurate haptic guidance and the calibration of robot kinematic models using operator's intention instead of relying on exteroceptive sensors. This is used to improve online haptic guidance in the context of shared control, or to progress towards automatic task completion after an initial learning phase. The method presented in this paper is based on an optimization in the task space to minimize the errors between the executed and desired trajectories, both estimated from models. This approach is particularly relevant when the execution of a planned task would suffer from errors that exteroceptive measurements could not fully correct, because of sensor inaccuracy or unavailability. A user study realized for a drawing task is detailed to illustrate that initially inaccurate task registration and robot models can be corrected from user inputs only. The results show that the proposed algorithm can learn the correct models, which in turns significantly improves the quality of the haptic guidance and decreases path deviations during the teleoperated task.