Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment

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 journalJournal articleResearchpeer-review

1 Citation (Scopus)
62 Downloads (Pure)

Abstract

The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker.
Original languageEnglish
Article number11610
JournalApplied Sciences
Volume12
Issue number22
Number of pages19
ISSN1454-5101
DOIs
Publication statusPublished - 15 Nov 2022

Keywords

  • Disassembly
  • collaborative robots
  • collision avoidance
  • contact-rich task
  • machine learning
  • reinforcement learning

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