Transparent liquid manipulation in robotic pouring remains challenging for perception systems: specular/refraction effects and lighting variability degrade visual cues, undermining reliable level estimation. To address this challenge, we introduce RadarEye, a real-time mmWave radar signal processing pipeline for robust liquid level estimation and tracking during the whole pouring process. RadarEye integrates (i) a high-resolution range-angle beamforming module for liquid level sensing and (ii) a physics-informed mid-pour tracker that suppresses multipath to maintain lock on the liquid surface despite stream-induced clutter and source container reflections. The pipeline delivers sub-millisecond latency. In real-robot water-pouring experiments, RadarEye achieves a 0.35 cm median absolute height error at 0.62 ms per update, substantially outperforming vision and ultrasound baselines.

翻译：机器人倾倒过程中的透明液体操控对感知系统仍具挑战性：镜面反射/折射效应与光照变化会削弱视觉线索，从而影响液位估计的可靠性。为应对这一挑战，我们提出了RadarEye——一种用于在整个倾倒过程中实现鲁棒液位估计与追踪的实时毫米波雷达信号处理流程。RadarEye整合了（i）用于液位感知的高分辨率距离-角度波束成形模块，以及（ii）基于物理建模的倾倒过程追踪器，该追踪器通过抑制多径效应，能够在液体流引发的杂波及源容器反射干扰下持续锁定液面。该处理流程可实现亚毫秒级延迟。在实际机器人倒水实验中，RadarEye以每次更新0.62毫秒的速度实现了0.35厘米的绝对高度误差中位数，显著优于视觉与超声波基线方法。