A review on reinforcement learning for contact-rich robotic manipulation tasks

Íñigo Elguea-Aguinaco*, Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Ibai Inziarte-Hidalgo, Simon Bøgh, Nestor Arana-Arexolaleiba

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

39 Citations (Scopus)
320 Downloads (Pure)

Abstract

Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.

Original languageEnglish
Article number102517
JournalRobotics and Computer-Integrated Manufacturing
Volume81
ISSN0736-5845
DOIs
Publication statusPublished - 27 Dec 2022

Bibliographical note

Funding Information:
This work has been partially funded by the Basque Government Department of Economic Development, Sustainability and Environment, Spain through the Bikaintek 2020 program, by the FEDER, Spain 2014–2020 Operational Program (GA 0011-1365-2020-000285) “Célula robótica para el pulido de piezas estructurales de metal duro del sector aeronáutico - ARISTARCO” and by the H2020-WIDESPREAD (GA 857061) “Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing - R2P2”.

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Contact-rich manipulation
  • Deformable object manipulation
  • Industrial manipulators
  • Reinforcement learning
  • Rigid object manipulation

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