From human-human collaboration to human-robot collaboration: automated generation of assembly task knowledge model

You, Yingchao, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Yang, Xintong ORCID: https://orcid.org/0000-0002-7612-614X and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2022. From human-human collaboration to human-robot collaboration: automated generation of assembly task knowledge model. Presented at: 27th IEEE International Conference on Automation and Computing (ICAC2022), Bristol, UK, 1-3 Sept 2022. 2022 27th International Conference on Automation and Computing (ICAC). IEEE, 10.1109/ICAC55051.2022.9911131

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Abstract

Task knowledge is essential for robots to proactively perform collaborative assembly tasks with a human partner. Representation of task knowledge, such as task graphs, robot skill libraries, are usually manually defined by human experts. In this paper, different from learning from demonstrations of a single agent, we propose a system that automatically constructs task knowledge models from dual-human demonstrations in the real environment. Firstly, we track and segment video demonstrations into sequences of action primitives. Secondly, a graph-based algorithm is proposed to extract structure information of a task from action sequences, with task graphs as output. Finally, action primitives, along with interactive information between agents, temporal constraints, are modelled into a structured semantic model. The proposed system is validated in an IKEA table assembly task experiment.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	78-1-6654-9807-4
Date of First Compliant Deposit:	19 July 2022
Date of Acceptance:	7 July 2022
Last Modified:	03 Jan 2023 10:06
URI:	https://orca.cardiff.ac.uk/id/eprint/151367

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