Real-time trajectory/profile learning for robots in human-robot interactions

In the process of human-robot interaction, effective representation and real-time learning of manipulator's trajectory/profile in response to human's motion are presented. Method of obtaining approximate solutions during the learning stage are introduced to circumvent the noise effect caused by numerical inaccuracy and computational errors. Perturbed solutions are derived as an alternative approach to overcome the noise effect. Simulation examples are given to illustrate every stage of the presentation.