Hierarchical Reinforcement Learning using Gaussian Random Trajectory Generation in Autonomous Furniture Assembly

Won Joon Yun, David Mohaisen, Soyi Jung, Jong Kook Kim, Joongheon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. The furniture assembly problem is formulated as a comprehensive human-like long-horizon manipulation task that requires a long-term planning and a sophisticated control. Our proposed model, GRT-HL, draws inspirations from the semi-supervised adversarial autoencoders, and learns latent representations of the position trajectories of the end-effector. The high-level policy generates an optimal trajectory for furniture assembly, considering the structural limitations of the robotic agents. Given the trajectory drawn from the high-level policy, the low-level policy makes a plan and controls the end-effector. We first evaluate the performance of GRT-HL compared to the state-of-the-art reinforcement learning methods in furniture assembly tasks. We demonstrate that GRT-HL successfully solves the long-horizon problem with extremely sparse rewards by generating the trajectory for planning.

Original languageEnglish
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages3624-3633
Number of pages10
ISBN (Electronic)9781450392365
DOIs
Publication statusPublished - 2022 Oct 17
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: 2022 Oct 172022 Oct 21

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States
CityAtlanta
Period22/10/1722/10/21

Keywords

  • assembly control
  • hierarchical reinforcement learning
  • reinforcement learning
  • robotics

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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