# Perspectives and Challenges in Robotic Neurorehabilitation
## 3 Current Trends of Robot-Aided Neurorehabilitation
### Relhabilitative Training
#### 3.1.1 Advances in Motor Control for Robotic Neurorehabilitation  
Understanding how the brain controls movements and which are the applied mechanisms to learn new skills is fundamental to plan an effective robot-based therapy, aimed at the promotion of sensorimotor recovery. In this framework, the design of the most appropriate and effective control strategy (i.e., the most sophisticated assistance) plays a crucial role, and can be achieved by exploiting and properly applying the latest findings in human neuroscience. Many studies in the field of human physiology, biomechanics, and motor control have developed key theories that attempt to provide a complete understanding on how we plan and generate movements. The first neuroscientific result to exploit sensorimotor neurorehabilitation relates to how motor variability plays a crucial role in promoting motor learning. While originally variability was seen as a \"noise\" that the brain had to reduce during learning [138], current studies have started to understand its value in the acquisition of motor skills (i.e., a higher exploration of the dynamical manifold of the environment). Specifically, [PERSON] et al. [113] demonstrated how variability before learning a task is associated with faster rates of adaptation in both error-based and reinforcement tasks. Other studies tried to go further, separating variability on the basis of its effect on task performance [139] and, more recently, different amounts of variability have emerged as differently affecting the learning process, if applied in different spaces (task space vs. null space) of the dynamical manifold [140]. All these results suggest that the design of robotic rehabilitation protocols that introduce variability in the task during the training phase (see, as an example, the patient cooperative control strategy that is described above) may facilitate learning and thus sensorimotor recovery. The second neuroscientific hint that is worth considering is related to action observation. It has been extensively demonstrated that visual feedback received from the observation of an action performed by another person contributes to improving motor learning [141]. [PERSON] and [PERSON] [142] revealed that when subjects observe a video of an individual performing reaching movements in a challenging environment and then perform the task by themselves, their performances in terms of learning are higher than those of individuals who did not observe any video. As a possible explanation, the authors suggested that action observation could be related to the acquisition of a neural representation of the dynamics of the task, as further discussed and also supported by other experiments [143; 144]. It is therefore reasonable to believe that the integration of action observation phases in robotic neurorehabilitation programs may lead to more consistent improvements. Based on these findings, action observation and passive proprioceptive training have been performed together, introducing here an additional point to discuss: The role of proprioceptive learning (or more generally, sensory learning) in the recovery process. Sensory learning is closely related to motor learning in the way in which motor learning shapes sensory networks in the brain and sensory learning changes motor areas [145; 146]. In particular, sensory learning results in changes to motor networks in the brain and is also associated with plasticity in sensory systems that is determined by afferent inputs from the periphery and cortical projections from motor areas. Limited to proprioception, the idea to use robots to improve the recovery of this sensory channel has been extensively tested [145; 146], in addition with supplementary vibro-tactile feedback [147]. Despite all the above-mentioned findings, we still do not have any information about the mechanisms adopted by the brain in the sensorimotor recovery process. New tools that can be applied to address this issue are models that belong to the field of computational neurorehabilitation.