Optimized Fuzzy Impedance Control for Empowering Human in Execution of Onerous Task with Manipulators Loris Roveda1, Alessio Prini1, Tito Dinon1, Shaghayegh Haghshenas1,2, Nicola Pedrocchi1, Francesco Braghin2 and Lorenzo Molinari Tosatti1 Abstract— The proposed contribution describes a fuzzy impedance control based approach to assist the human operator in industrial applications while executing onerous tasks. The developed method allows to set in real-time the set-point of the impedance control on the basis of the humans intentions. Moreover, an off-line optimization of the fuzzy impedance con- trol parameters has been performed through an optimization algorithm based on a neural network approach. The proposed method has been veriﬁed in an experimental procedure with 20 human subjects interacting with the manipulator (KUKA iiwa 14 R820). The proposed algorithm has been also validated in an industrial task, performing the installation of a heavy and bulky hatrack component, case-study related to the H2020 CleanSky 2 EURECA project at the KUKA Innovation Award 2017. I. INTRODUCTION Human-robot interaction (HRI) is one of the most relevant topics since the early stage of robotics research [1]. The co- operative execution of onerous tasks (e.g., lifting/installation of heavy components [2]) is one of the main goal of human- robot cooperation research, in order to improve the task ergonomy to avoid/limit musculoskeletal disorders [3]. Many control schemes have been developed to improve the human capabilities while executing cooperative industrial tasks in interaction with standard industrial manipulators [4]. How- ever, only few proposed methods face the problem of lifting unknown inertia properties objects (very common scenario in industrial applications where different components have to be manipulated). Moreover, the applied force of the human operator (to the robot or to the manipulated component) is continuously required even to statically compensate for the component weight. 1 IRAS group of the Institute of Industrial Technologies and Automation of the National Council of Research (ITIA-CNR) via Corti 12, 20133, Milano, Italy loris.roveda@itia.cnr.it 2 Department of Mechanical Engineering, Politecnico of Milano via La Masa 1, 20158, Milano, Italy Fig. 1. Force derivative membership function. The proposed contribution describes a fuzzy impedance control based approach to assist the human operator in industrial applications while executing onerous tasks (e.g., lifting heavy loads). The developed method allows to set in real-time the set-point of the impedance control on the basis of the humans intentions. Two fuzzy membership func- tions have been deﬁned, respectively, on the basis of force derivative and velocity signals to describe human intention. Such membership functions allow to on- line calculate an assistance level through the task execution, deforming the impedance control set-point to enhance the capabilities of the human operator. Moreover, an off-line optimization of such membership functions, together with the impedance control parameters (i.e., stiffness and damping parameters) has been performed through an optimization algorithm consisting of neural networks to automatically tune all impedance con- trol parameters for a speciﬁc component by minimization of standard normalized jerk-related indexes. The proposed method has been veriﬁed in an experimental procedure where 20 human subjects interact with the manipulator (KUKA iiwa 14 R820) to collaboratively lift a heavy component while evaluating the task by scoring seven different criteria (including naturalness, smoothness, detection of intention, motion, stability, effort and performance). During the valida- tion procedure, the developed control strategy has been com- pared with the standard impedance controller. Experimental results show the capabilities of the designed control strategy including both fuzzy impedance control and the optimization algorithm in emp