Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization
(چکیده مقاله) :
Abstract :
Robots are increasingly exploited in production plants. Within the Industry 4.0 paradigm, the robot
complements the human’s capabilities, learning new tasks and adapting itself to compensate for
uncertainties. With this aim, the presented paper focuses on the investigation of machine learning
techniques to make a sensorless robot able to learn and optimize an industrial assembly task.
Relying on sensorless Cartesian impedance control, two main contributions are defined: (1) a tasktrajectory
learning algorithm based on a few human’s demonstrations (exploiting Hidden Markov
Model approach), and (2) an autonomous optimization procedure of the task execution (exploiting
Bayesian Optimization). To validate the proposed methodology, an assembly task has been selected as
a reference application. The task consists of mounting a gear into its square-section shaft on a fixed
base to simulate the assembly of a gearbox. A Franka EMIKA Panda manipulator has been used as a
test platform, implementing the proposed methodology. The experiments, carried out on a population
of 15 subjects, show the effectiveness of the proposed strategy, making the robot able to learn and
optimize its behavior to accomplish the assembly task, even in the presence of task uncertainties.
complements the human’s capabilities, learning new tasks and adapting itself to compensate for
uncertainties. With this aim, the presented paper focuses on the investigation of machine learning
techniques to make a sensorless robot able to learn and optimize an industrial assembly task.
Relying on sensorless Cartesian impedance control, two main contributions are defined: (1) a tasktrajectory
learning algorithm based on a few human’s demonstrations (exploiting Hidden Markov
Model approach), and (2) an autonomous optimization procedure of the task execution (exploiting
Bayesian Optimization). To validate the proposed methodology, an assembly task has been selected as
a reference application. The task consists of mounting a gear into its square-section shaft on a fixed
base to simulate the assembly of a gearbox. A Franka EMIKA Panda manipulator has been used as a
test platform, implementing the proposed methodology. The experiments, carried out on a population
of 15 subjects, show the effectiveness of the proposed strategy, making the robot able to learn and
optimize its behavior to accomplish the assembly task, even in the presence of task uncertainties.
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2021
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 18 صفحه
سال انتشار: 2021
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 18 صفحه
Authors / Descriptions(نویسندگان/توضیحات): مقاله ISI سال انتشار: 2021 / نویسندگان: Loris Roveda , Mauro Magni b, Martina Cantoni b, Dario Piga a, Giuseppe Bucca
Sent date(تاریخ ارسال) :
1400/01/12 | 4/1/2021
Number of visits(تعداد بازدید):
772
Key words (کلمات کلیدی):
Machine learning , Human–robot , Human–robot collaboration
Number of pages(تعداد صفحات) :
18
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