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Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

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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 human-robot 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.

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Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement N∘ 779963).

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Authors and Affiliations

  1. Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy

    Loris Roveda, Paolo Franceschi, Arash Abdi, Lorenzo Molinari Tosatti & Nicola Pedrocchi

  2. Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI) IDSIA-SUPSI, Manno, 6928, Switzerland

    Loris Roveda

  3. School of Industrial and Information Engineering Politecnico di Milano, Milano, 20156, Italy

    Jeyhoon Maskani & Francesco Braghin

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  1. Loris Roveda
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Roveda, L., Maskani, J., Franceschi, P. et al. Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration. J Intell Robot Syst 100, 417–433 (2020). https://doi.org/10.1007/s10846-020-01183-3

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