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An Perception Enhanced Human-Robot Skill Transfer Method for Reactive Interaction

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 15052))

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Abstract

In this paper, we propose a trust human-robot skill transfer framework, interpretive and reactive dynamic system (IRDS), by investigating the behaviour tree (BT) and dynamic movement primitives (DMPs) enhanced by the feedback from perceived information for dynamic and uncertain tasks and environments. Human sensorimotor control allows interactions with various environments and accomplishes complex manipulation tasks with uncertainty; however, it is still hard for robots to own this capability. In this work, we aim to transfer these reactive skills to robots, which enable robots to interact with humans and environments under varying uncertainty. The main challenges of robot skill learning are the generalization, safety and stability issues during the skills learning and execution autonomously. BT was investigated for task planning and reactive interaction during the robot execution. The dynamic system-based model generates action for the low-level compliant controller. The convergence of the proposed IRDS framework under dynamic interaction and disturbance was proved by control theory. We conducted simulations and physical experiments on the real robots to evaluate the generalization performance and the convergence capability under uncertainty. And the results show that the reactivity, convergence and interaction performance can be guaranteed, and the learned skills can be transferred among different physical robot platforms.

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Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Weiyong Si

  2. South China University of Technology, Guangzhou, 510640, China

    Jiale Dong

  3. Bristol Robotics Lab, University of the West of England, Bristol, BS16 1QY, UK

    Ning Wang

  4. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Chenguang Yang

Authors
  1. Weiyong Si

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  2. Jiale Dong

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  3. Ning Wang

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  4. Chenguang Yang

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Corresponding author

Correspondence toChenguang Yang.

Editor information

Editors and Affiliations

  1. Department of Electronic and Electrical Engineering, Brunel University London, London, UK

    M. Nazmul Huda

  2. Department of Mechanical and Aerospace Engineering, Brunel University London, Uxbridge, UK

    Mingfeng Wang

  3. Department of Electronic and Electrical Engineering, Brunel University London, London, UK

    Tatiana Kalganova

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Si, W., Dong, J., Wang, N., Yang, C. (2025). An Perception Enhanced Human-Robot Skill Transfer Method for Reactive Interaction. In: Huda, M.N., Wang, M., Kalganova, T. (eds) Towards Autonomous Robotic Systems. TAROS 2024. Lecture Notes in Computer Science(), vol 15052. Springer, Cham. https://doi.org/10.1007/978-3-031-72062-8_6

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