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Cited by 80Robot Manipulation and LearningReinforcement Learning in RoboticsProsthetics and Rehabilitation Robotics

Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization

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Roveda, Loris, Magni, Mauro, Cantoni, Martina, Piga, Dario, Bucca, Giuseppe·2021·Robotics and Autonomous Systems
DOI 10.1016/j.robot.2020.103711
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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 task-trajectory 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.

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Cited by

Sorted by citations

Authors
Roveda, L., Magni, M., Cantoni, M., Piga, D., & Bucca, G.
Affiliations
  • University of Applied Sciences and Arts of Southern Switzerland (CH)
  • Politecnico di Milano (IT)
Corresponding author
Roveda, Loris
ORCID
Venue
Robotics and Autonomous Systems
Publisher
Elsevier BV
Published
2021-02
Volume
136
Pages
103711
ISSN
0921-8890 (ISSN-L)
DOI
10.1016/j.robot.2020.103711
OpenAlex
W3035497536
Cited by
80
References
3
Citations by year
2026: 8 · 2025: 13 · 2024: 10 · 2023: 14 · 2022: 15 · 2021: 20
FWCI
5.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Robotics and Autonomous Systems (publishedVersion, cc-by-nc-nd)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by-nc-nd)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
Horizon 2020 Framework Programme
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
Keywords
human-robot collaboration, industrial assembly, Cartesian impedance control, hidden Markov models, Bayesian optimization, Franka Emika Panda, Industry 4.0

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