One of the most crucial and challenging steps in the development of robots intended to restore the mobility of the human body after a loss of functional movement due to neurological injuries is the IK of physiological limbs, which consists of computing joint angles configuration based on the predefined input workspace coordinates. Generally speaking, the complexity of the IK problem depends on the geometry of the manipulator and the nonlinearity of its model, which gives the corresponding relation between the task and the joint spaces. Furthermore, IK solution is essential for the real-time control. Thus, it must be precise in order to enable the robot to perform the task successfully. IK techniques can be classified into three categories, namely, analytical method, numerical method, and intelligent method. The analytical method solves IK by solving a set of closed-form equations that can give the generalized coordinate value that drives the end effector of the manipulator to the predefined target position [1].

Being virtually hooked up to a human could help robots respond to disasters or other situations that would put human responders' lives at risks. The researchers say that a system like this could be used to help in robotic clean-up operations such as the one after the Fukushima Daiichi nuclear power plant disaster in Japan in 2011. Humans could have guided robots to navigate around the site more accurately, from a safe distance. And while there's currently no machine learning involved in the process, Ramos believes the data captured from the system could be used to help train autonomous robots.