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A residual reinforcement learning method for robotic assembly using visual and force information
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
KTH, School of Industrial Engineering and Management (ITM), Production engineering.ORCID iD: 0000-0001-8679-8049
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2024 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 72, p. 245-262Article in journal (Refereed) Published
Abstract [en]

Robotic autonomous assembly is critical in intelligent manufacturing and has always been a research hotspot. Most previous approaches rely on prior knowledge, such as geometric parameters and pose information of the assembled parts, which are hard to estimate in unstructured environments. This paper proposes a residual reinforcement learning (RL) policy for robotic assembly via combining visual and force information. The residual RL policy, which consists of a visual-based policy and a force-based policy, is trained and tested in an end-to-end manner. In the assembly procedure, the visual-based policy focuses on spatial search, while the force-based policy handles the interactive behaviors. The experimental results reveal the high sample efficiency of our approach, which exhibits the ability to generalize across diverse assembly tasks involving variations in geometries, clearances, and configurations. The validation experiments are conducted both in simulation and on a real robot.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 72, p. 245-262
Keywords [en]
Compliance control, Residual reinforcement learning, Robotic assembly, Visual and force perception
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-341697DOI: 10.1016/j.jmsy.2023.11.008ISI: 001140126900001Scopus ID: 2-s2.0-85179753333OAI: oai:DiVA.org:kth-341697DiVA, id: diva2:1823054
Note

QC 20231229

Available from: 2023-12-29 Created: 2023-12-29 Last updated: 2024-02-01Bibliographically approved

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Wang, Lihui

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf