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：遥操作为远程控制与机器人本体感知数据采集提供了一种前景广阔的模式。尽管近期取得进展，现有遥操作系统在效率和人机工效方面仍存在局限，尤其在复杂场景中。本文提出CaFe-TeleVision——一种融合粗到精控制与沉浸式情境可视化的遥操作系统，旨在提升人机工效。其核心在于重定向模块提出的粗到精控制机制，通过弥合作业空间差异协同优化操作效率与物理工效。为向人类视觉系统提供具备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，有效降低多视图处理的认知负荷。该系统基于仿人协作机器人构建，并通过六项挑战性双手操作任务进行验证。针对24名参与者的用户研究证实，CaFe-TeleVision在统计学意义上显著改善了人机工效，表现为更低的操作负荷与更高的用户接受度。定量结果同时验证了本系统在六项任务中的优越性能：成功率最高提升28.89%，任务完成时间加速26.81%。项目主页：https://clover-cuhk.github.io/cafe_television/