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/

翻译：遥操作为远程控制和机器人本体感知数据采集提供了一种前景广阔的范式。尽管近期取得了进展，但当前的遥操作系统在效率和人体工程学方面仍存在局限，尤其是在具有挑战性的场景中。本文提出CaFe-TeleVision，一种具有沉浸式情境可视化功能的从粗到精遥操作系统，旨在提升人机工程学性能。其核心在于，在重定向模块中提出了一种从粗到精的控制机制，以弥合工作空间差异，共同优化效率和物理人机工程学。为了向人类视觉系统提供具有充分视觉线索的沉浸式反馈，感知模块集成了按需情境可视化技术，从而降低了多视图处理的认知负荷。该系统构建于一个人形协作机器人之上，并通过六项具有挑战性的双手操作任务进行了验证。一项涉及24名参与者的用户研究证实，CaFe-TeleVision在统计学意义上显著提升了人机工程学性能，表现为在遥操作期间任务负荷更低、用户接受度更高。定量结果也验证了我们的系统在六项任务中的优越性能，其成功率最高超出对比方法28.89%，完成时间缩短了26.81%。项目网页：https://clover-cuhk.github.io/cafe_television/