CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics

IEEE Robotics and Automation Letters (RA-L) 2025

Zixin Tang         Yiming Chen         Quentin Rouxel         Dianxi Li         Shuang Wu         Fei Chen Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong logo clover logo cuhk
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Abstract

overview

Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision, a coarse-to-fine teleoperation system with immersive situated visualization for enhanced ergonomics. At its core, a coarse-to-fine control mechanism is proposed in the retargeting module to bridge workspace disparities, jointly optimizing efficiency and physical ergonomics. To stream immersive feedback with adequate visual cues for human vision systems, an on-demand situated visualization technique is integrated in the perception module, which reduces the cognitive load for multi-view processing. The system is built on a humanoid collaborative robot and validated with six challenging bimanual manipulation tasks. User study among 24 participants confirms that CaFe-TeleVision enhances ergonomics with statistical significance, indicating a lower task load and a higher user acceptance during teleoperation. Quantitative results also validate the superior performance of our system across six tasks, surpassing comparative methods by up to 28.89% in success rate and accelerating by 26.81% in completion time.

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BibTeX

Paper pages 
@article{tang2025cafetelevision,
  title={CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics}, 
  author={Tang, Zixin and Chen, Yiming and Rouxel, Quentin and Li, Dianxi and Wu, Shuang and Chen, Fei},
  journal={IEEE Robotics and Automation Letters},
  year={2025},
  publisher={IEEE}
}