Extracting and Transferring Hierarchical Knowledge to Robots Using Virtual Reality

Zhenliang Zhang, Jie Guo, Dongdong Weng, Yue Liu, Yongtian Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

We study the knowledge transfer problem by training a task of folding clothes in the virtual world using an Oculus Headset and validating with a physical Baxter robot. We argue such complex transfer is realizable if an abstract graph-based knowledge representation is adopted to facilitate the process. An And-Or-Graph (AOG) grammar model is introduced to represent the knowledge, which can be learned from the human demonstrations performed in the Virtual Reality (VR), followed by the case analysis of folding clothes represented and learned by the AOG grammar model. In the experiment, the learned knowledge from the given six virtual scenarios is implemented on a physical robot platform, demonstrating that the grammar-based knowledge is an effective representation.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages669-670
Number of pages2
ISBN (Electronic)9781728165325
DOIs
Publication statusPublished - Mar 2020
Event2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020 - Atlanta, United States
Duration: 22 Mar 202026 Mar 2020

Publication series

NameProceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020

Conference

Conference2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
Country/TerritoryUnited States
CityAtlanta
Period22/03/2026/03/20

Keywords

  • Human computer interaction (HCI)
  • Human-centered computing
  • Interaction paradigms
  • Virtual reality

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