Adaptive Human-Robot Physical Interaction for Robot Coworkers

Abstract This chapter describes research that aims to develop theories, methods, and tools to understand the mechanisms of neuromotor adaptation in human-robot physical interaction. Human power-assisting systems, eg, powered lifting devices that aid human operators in manipulating heavy or bulky loads, require physical contact between the operator and machine, hence creating a coupled dynamic system. This coupling has been shown to be prone to inherent instabilities and performance degradation due to a change in human arm stiffness; when instability is encountered, a human operator often attempts to control the oscillation by stiffening their arm, which leads to a stiffer system with more instability. The goal of the research is to develop control algorithms for robot coworkers that adaptively adjust the contact impedance between the operator and robotic manipulator in order to achieve higher performance and stability. To this end, this chapter will discuss how to: (1) understand the association between neuromuscular adaptations and system performance limits, (2) develop probabilistic methods to classify and predict the transition of the operator's physical and/or cognitive states from physiological measurements, and (3) integrate this knowledge into a structure of shared human-robot control.