Abstract
In this paper, we present a novel learning-based shared control framework. This framework deploys first-order Dynamical Systems (DS) as motion generators providing the desired reference motion, and a Variable Stiffness Dynamical Systems (VSDS) (Chen et al. 2021) for haptic guidance. We show how to shape several features of our controller in order to achieve authority allocation, local motion refinement, in addition to the inherent ability of the controller to automatically synchronize with the human state during joint task execution. We validate our approach in a teleoperated task scenario, where we also showcase the ability of our framework to deal with situations that require updating task knowledge due to possible changes in the task scenario, or changes in the environment. Finally, we conduct a user study to compare the performance of our VSDS controller for guidance generation to two state-of-the-art controllers in a target reaching task. The result shows that our VSDS controller has the highest successful rate of task execution among all conditions. Besides, our VSDS controller helps reduce the execution time and task load significantly, and was selected as the most favorable controller by participants.
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Acknowledgements
The authors thank Katrin Schulleri for her input on the statistical analysis.
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Open Access funding enabled and organized by Projekt DEAL. This work was partially funded by the Deutsche Forschungsgemeinschaft(DFG)-SPP projects DELIGHT and SOLAR.
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All authors contributed to the approach conception and design. Material preparation, data collection and analysis were performed by Haotian Xue and Youssef Michel. The first draft of the manuscript was written by Haotian Xue and Youssef Michel under the supervision of Dongheui Lee. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript
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Xue, H., Michel, Y. & Lee, D. A Shared Control Approach Based on First-Order Dynamical Systems and Closed-Loop Variable Stiffness Control. J Intell Robot Syst 109, 85 (2023). https://doi.org/10.1007/s10846-023-02023-w
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DOI: https://doi.org/10.1007/s10846-023-02023-w