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We present a novel robotic task-practice system, i.e., adaptive and automatic presentation of tasks (ADAPT), which is designed to enhance the recovery of upper extremity functions in patients with stroke. We designed ADAPT in accordance with current training guidelines for stroke rehabilitation; ADAPT engages the patient intensively, actively, and adaptively in a variety of realistic functional tasks that require reaching and manipulation. A general-purpose robot simulates the dynamics of the functional tasks and presents these functional tasks to the patient. A novel tool-changing system enables ADAPT to automatically switch between the tools corresponding to the functional tasks. The control architecture of ADAPT is composed of three main components: a high-level task scheduler, a functional task model, and a low-level admittance controller. The high-level task scheduler adaptively selects the task to practice and sets the task difficulty based on the previous performance of the patients. The functional task model generates desired trajectories based on learned models of task dynamics. Tasks dynamics are modeled with receptive field weighted regression (RFWR), such that the feel of the task tools is accurately modeled, and the task difficulty can be easily adjusted. The low-level admittance controller, which is also learned with RFWR, implements the selected task trajectory for robot-patient interaction. The results of a preliminary experiment with a healthy subject demonstrate the successful operation of ADAPT.