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Autonomous Robots

, Volume 43, Issue 8, pp 2055–2069 | Cite as

Motion encoding with asynchronous trajectories of repetitive teleoperation tasks and its extension to human-agent shared teleoperation

  • Affan PervezEmail author
  • Hiba Latifee
  • Jee-Hwan Ryu
  • Dongheui Lee
Article
  • 283 Downloads

Abstract

Teleoperating a robot for complex and intricate tasks demands a high mental workload from a human operator. Deploying multiple operators can mitigate this problem, but it can be also a costly solution. Learning from Demonstrations can reduce the human operator’s burden by learning repetitive teleoperation tasks. Yet, the demonstrations via teleoperation tend to be inconsistent compared to other modalities of human demonstrations. In order to handle less consistent and asynchronous demonstrations effectively, this paper proposes a learning scheme based on Dynamic Movement Primitives. In particular, a new Expectation Maximization algorithm which synchronizes and encodes demonstrations with high temporal and spatial variances is proposed. Furthermore, we discuss two shared teleoperation architectures, where, instead of multiple human operators, a learned artificial agent and a human operator share authority over a task while teleoperating cooperatively. The agent controls the more mundane and repetitive motion in the task whereas human takes charge of the more critical and uncertain motion. The proposed algorithm together with the two shared teleoperation architectures (human-synchronized and agent-synchronized shared teleoperation) has been tested and validated through simulation and experiments on 3 Degrees-of-Freedom Phantom-to-Phantom teleoperation. Conclusively, the both proposed shared teleoperation architectures have shown superior performance when compared with the human-only teleoperation for a peg-in-hole task.

Keywords

Dynamic movement primitives Learning from demonstrations Teleoperation Human-agent shared teleoperation Cooperative teleoperation Human-synchronized Agent-synchronized Haptic feedback 

Notes

Supplementary material

Supplementary material 1 (mp4 104249 KB)

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringTechnical University of Munich (TUM)MunichGermany
  2. 2.Department of Mechanical EngineeringKorea University of Technology and EducationCheonanSouth Korea
  3. 3.Institute of Robotics and MechatronicsGerman Aerospace Center (DLR)CologneGermany

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