Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human's fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved humanrobot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system.

The paper describes a human-robot cooperative installation methodology of heavy and bulky components based on marker-based visual servoing, force control, and human-robot cooperation. The main advance in the human-robot cooperation is achieved by a shared-control of the interaction during the installation task, relieving the human operator by the manipulated load and giving to the robot a partially autonomous behaviour in the force-tracking direction. Experimental results are shown in the context of the H2020 CleanSky 2 EURECA project in which a side-wall panel is installed in a 1:1 scale mock-up scenario of an A320 plane fuselage environment.

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The problem addressed in this work is the arbitration of the role between a robot and a human during physical Human-Robot Interaction, sharing a common task. The system is modeled as a Cartesian impedance, with two separate external forces provided by the human and the robot. The problem is then reformulated as a Cooperative Differential Game, which possibly has multiple solutions on the Pareto frontier. Finally, the bargaining problem is addressed by proposing a solution depending on the interaction force, interpreted as the human will to lead or follow. This defines the arbitration law and assigns the role of leader or follower to the robot. Experiments show the feasibility and capabilities of the proposed control in managing the human-robot arbitration during a sharedtrajectory following task.

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