.png){kind=icon}
.png){kind=icon}
Return
Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration
.png){kind=icon}
OA
AI
DOI：10.1007/s10846-020-01183-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
Human-robot collaboration
Machine learning
Industry 4
0
Model-based reinforcement learning control
Variable impedance control
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K