通过粗到精控制与沉浸式视觉反馈，提升人形机器人遥操作的效率与舒适度
CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics
- 采用粗到精控制机制，缓解工作空间差异带来的操作不适
- 在6个双臂任务中，成功率提升28.89%，完成时间加快26.81%
- 适合需要高精度、低疲劳的远程操控场景，如工业协作或救援
遥操作为远程控制与机器人本体感知数据采集提供了一种有前景的范式。尽管近期取得进展，现有系统在效率与人体工学方面仍存在局限，尤其在复杂场景下表现不佳。本文提出CaFe-TeleVision，一种具有沉浸式情境化视觉反馈的粗到精遥操作系统，以增强人体工学体验。核心在于，在重映射模块中引入粗到精控制机制，有效弥合工作空间差异，兼顾操作效率与身体舒适性。感知模块集成按需情境化可视化技术，为人类视觉系统提供充分的视觉线索，降低多视角处理的认知负担。系统基于人形协作机器人构建，并在六个挑战性双臂操作任务上进行验证。24名参与者组成的用户研究证实，该系统显著改善人体工学表现，任务负荷更低，用户接受度更高。定量结果表明，系统在六项任务中均优于对比方法，成功率达28.89%提升，完成时间缩短26.81%。
原文摘要 · Abstract (English)
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. Project webpage: https://clover-cuhk.github.io/cafe_television/
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