Human Like Adaptation of Force and Impedance in Stable and Unstable Tasks

Abstract—This paper presents a novel human-like learning con-troller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, with-out requiring interaction force sensing. Index Terms—Feedforward force, human motor control, impedance, robotic control. I

Animal-Computer Interaction: the emergence of a discipline

In this editorial to the IJHCS Special Issue on Animal-Computer Interaction (ACI), we provide an overview of the state-of-the-art in this emerging field, outlining the main scientific interests of its developing community, in a broader cultural context of evolving human-animal relations. We summarise the core aims proposed for the development of ACI as a discipline, discussing the challenges these pose and how ACI researchers are trying to address them. We then introduce the contributions to the Special Issue, showing how they illustrate some of the key issues that characterise the current state-of-the-art in ACI, and finally reflect on how the journey ahead towards developing an ACI discipline could be undertaken

Characterization of time-varying human operator dynamics

Human operator performance in tracking study of pilots determined by deterministic characterization theory and mathematical mode