frobt-06-00099.pdf (2.33 MB)
Symbolic-based recognition of contact states for learning assembly skills
journal contribution
posted on 2019-09-26, 15:05 authored by Ali Al-Yacoub, Yuchen Zhao, Niels Lohse, Yee GohYee Goh, Peter KinnellPeter Kinnell, Pedro FerreiraPedro Ferreira, Ella-Mae HubbardElla-Mae HubbardImitation learning is gaining more attention because it enables robots to learn skills from human demonstrations. One of the major industrial activities that can benefit from imitation learning is the learning of new assembly processes. An essential characteristic of an assembly skill is its different contact states (CS). They determine how to adjust movements in order to perform the assembly task successfully. Humans can recognise CSs through haptic feedback. They execute complex assembly tasks accordingly. Hence, CSs are generally recognised using force and torque information. This process is not straightforward due to the variations in assembly tasks, signal noise and ambiguity in interpreting force/torque (F/T) information. In this research, an investigation has been conducted to recognise the CSs during an assembly process with a geometrical variation on the mating parts. The F/T data collected from several human trials were pre-processed, segmented and represented as symbols. Those symbols were used to train a probabilistic model. Then, the trained model was validated using unseen datasets. The primary goal of the proposed approach aims to improve recognition accuracy and reduce the computational effort by employing symbolic and probabilistic approaches. The model successfully recognised CS based only on force information. This shows that such models can assist in imitation learning.
Funding
Intelligent Automation Centre/Loughborough University (previously: the EPSRC Centre for Innovative Manufacturing in Intelligent Automation), under grant reference number EP/IO33467/1
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Frontiers in Robotics and AIVolume
6Publisher
Frontiers MediaVersion
- VoR (Version of Record)
Rights holder
© Al-Yacoub, Zhao, Lohse, Goh, Kinnell, Ferreira and HubbardAcceptance date
2019-09-26Publication date
2019-10-17Copyright date
2019eISSN
2296-9144Publisher version
Language
- en
Depositor
Dr Niels LohseArticle number
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