## 1 Introduction
Assistive strategies can be: impedance-based assistance that usually exploit the position information of the patient's arm in order to provide assistance and are typically called assist-as-needed; triggered that allows the subject to initiate the movement and the assist-as-needed approach starts only after a specific performance parameter has reached a threshold; weight counterbalancing which supports the arm and the robot weight; performance-based that evaluates online pre-determined performance indicators and adjusts the training parameters of interest accordingly [29]. All these factors analyzed above lead to the conclusion that the use of robot-assisted rehabilitation could induce increasing potential benefits to the child's recovery process, thus improving the damaged or loss sensorimotor functions. When these robots are used in clinical rehabilitation, the presence of a therapist plays a crucial role, not only to offer support during the training, but mainly because a key factor is the constant monitoring of child's posture, motivation and effort, the control of their tiredness and their level of attention. Presently, we can assume that the reason behind the increasing interest in rehabilitation robotics for the pediatric population is mainly due to the evidence provided by several studies which have showed how a greater functional improvement is acquired in robot-assisted rehabilitation compared to the conventional clinical treatments [13; 14; 30; 31]. Moreover, another key point in the use of robotic devices in the clinical setting is that, besides training, they have been used to evaluate the children's motor functions by integrating traditional clinical assessments with more objective, reliable and accurate measurements [32]. Indeed, the devices can collect the kinematic, kinetic and dynamic data, related to the upper limbs, during the robotic intervention itself, in order to characterize and classify subject's impairment and/or to define the effectiveness of robotic training [33]. Although there are examples of robots used for the functional assessment (see subsection on robotic assessment), in most of the studies examined, the evaluation of rehabilitation treatment is based on clinical scales. Among these, the most common are the following ones: the Quality of Upper Extremity Skills Test (QUEST) to evaluate movement patterns and hand function [34], the Modified Ashworth Scale (MAS) to measure the upper limb degree of spasticity based on the muscle resistance to passive stretches [35], the Fugl-Meyer Assessment (FMA) to quantitatively measure the sensorimotor impairments [36], the Melbourne Assessment of Unilateral Upper Limb Function (MAUULF) to assess the quality of the movements [37], the Besta Scale to evaluate both capacity and performance of children's hand [38], the Assisting Hand Assessment (AHA) or the Assisting Hand Assessment for children from 18 months to 12 year (Kids-AHA) to measure the ability of their affected hand while performing bimanual activities [39] and, finally, the Box and Block Test (BBT) to evaluate gross manual dexterity [40]. In conclusion, despite the outstanding development of high-tech devices, their application to pediatric neurorehabilitation and the interesting findings available so far, the key features and optimal control strategies that best enhance neuroplasticity and sensorimotor recovery in children with neurological diseases have not been defined yet and systematic approaches are still scarce. In this framework, the main motivation of this work is to critically review the existing literature related to robot-assisted rehabilitation protocols hitherto developed for the upper limbs of children with neurological disorders, with particular attention to their characteristics, control strategies, application (assessment and/or training) and results in clinical setting.