A Nonlinear Modeling Framework for Force Estimation in Human-Robot Interaction

Abstract

In modern applied research concerning human-machine interaction, the possibility of increasing the degree of intelligence and dexterity of the controlled plant by imitating human control dynamics has been a primary objective. To reach this goal, we propose a novel nonlinear modeling technique able to predict human force generated during a cooperative task with a controlled robot. The proposed Narmax model was constructed using an artificial neural network as a nonlinear functional approximator and was firstly trained offline with data acquired from ten subjects, performing a manipulation task on a small collaborative robot. Then, given the complexity of the system and the characteristics of human response, the exploitation of Peak to Peak Dynamics allowed the development of a reduced-order model that could reliably forecast the peak of human response. Ultimately, our human model was tested online on an industrial high-payload robot, showing its general applicability and how it can be used to let the robot anticipate human intention during collaborative manipulation.

Keywords

• Human-robot interaction
• human-in-the-loop
• Narmax
• human force estimation