"The human sensorimotor system enables the learning and control of an ever-growing number of artifacts ranging from simple tools such as chopsticks to advanced computer interfaces. The Researcher’s recent work in Japan exploited this human capacity to obtain dexterous skills on robots, which otherwise would require expert programming. In this framework, a human operator is put in the control loop of a robotic system where (s)he controls the robot in real-time. The operator then ‘learns’ to make the robot perform a given task (e.g. wiping clean a table by controlling a robot). This is analogous to a beginner’s learning to drive a car. After the operator becomes skilled, the signals coming in and leaving out of the robot are used to construct an autonomous controller. The key point of this framework is that it takes away the work from the cognitive system of an expert and places it on a layperson’s sensorimotor system. Up to now, the framework involved a sequential learning scheme: first the human operator learned to control the robot. Then, data was collected when the robot performed the tasked under the skilled operator guidance. Finally, this data was used to learn a policy using a machine learning technique. This project will substantially improve this framework by having the robot simultaneously learn with the human operator. The dynamics of the simultaneous learning of the human operator and the controlled robot will be studied in depth for obtaining quantitative criteria for a convergent learning system. The results of the analysis will be deployed on a robotic platform, and evaluation experiments will be carried out to show that convergent learning can be ensured leading to a virtuous learning experience for both the human and the robot. The development of this framework to its full potential will drastically change how we develop smart prosthetics and build robotic systems that can coexist with humans, for which this project offers an important contribution"