Stroke is a leading cause of serious long-term disability in developed countries, and has an enormous emotional and socioeconomic impact on patients, families and, health services. Upper-limb impairments and functional problems are, in fact, very common after a stroke. Impairments commonly include difficulty moving and co-ordinating the arms, hands and fingers, often resulting in difficulty carrying out Activities of Daily Living (ADLs) such as eating, dressing and washing. More than half of people with upper-limb impairment after stroke will still have difficulties in performing ADLs many months to years after their stroke. Robotic rehabilitation systems have the potential to deliver large doses of motor training in a cost-effective manner and, although the debate on the efficacy of robotic therapy is still open, they are emerging as valid solutions to help stroke survivors in the rehabilitation of the upper limb. Recent review of Norouzi-Gheidari shows that the effect of a robotic training is comparable to a conventional therapy training of the same length and intensity. A recent Cochrane systematic review of Mehrholz included 34 trials (involving 1160 participants) showed that electromechanical and robot-assisted arm training improved ADLs scores, arm function, and arm muscle strength, but the quality of the evidence was low to very low. Unfortunately, the mechanisms leading to impairment reduction following the robotic training are still unclear. It is known that neuroplasticity plays an important role in the motor recovery process of stroke patients and, further, that the patient should be engaged during the treatment in order to foster a process similar to motor learning. To promote engagement and maximize neuroplasticity, two main methods have been studied in robotic rehabilitation: i) the assist-as-needed training paradigm, and ii) the Detection of Patient Intent (DPI) method, also called guided force training. The first one, which consists in providing the minimal assistance needed to the subject to complete the task required, has shown promising results in enhancing the participation to the treatment, especially in medium-high functional patients. The DPI method, instead, is based on triggering the movement of the robot using the patient's exerted force or induced velocity. In some cases, the DPI method may even exploit biomedical signals like EMG or EEG to initiate the given task.