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

doi:10.1007/s10846-020-01183-3

2020-03-10

145

Lorenzo Molinari Tosatti

Nicola Pedrocchi

DOI：10.1007/s10846-020-01183-3

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摘要

En 中文

Industry 4.0is takinghuman-robot collaborationat 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 aModel-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 aModel Predictive Controller(MPC) withCross-Entropy Method(CEM). The aim of the MPC+CEM is to online optimize the stiffness and dampingimpedance controlparameters 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.

Keyword:

Human-robot collaboration

Machine learning

Industry 4

Model-based reinforcement learning control

Variable impedance control