Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration
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Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration Loris Roveda1,2 · Jeyhoon Maskani3 · Paolo Franceschi1 · Arash Abdi1 · Francesco Braghin3 · Lorenzo Molinari Tosatti1 · Nicola Pedrocchi1 Received: 22 November 2019 / Accepted: 19 February 2020 © Springer Nature B.V. 2020
Abstract 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. Keywords Human-robot collaboration · Machine learning · Industry 4.0 · Model-based reinforcement learning control · Variable impedance control
1 Introduction 1.1 Human-Robot Collaboration in Industry 4.0 Within the Industry 4.0 paradigm [1], a new concept of smart worker [2] is proposed. In such a scenario, the human operator is in charge of added-value tasks, management of the production process and supervision of the operations, being relieved from onerous tasks. To achieve such an Loris Roveda
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ambitious result, human-robot collaboration is a fundamental topic to be investigat
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