Toward Multimodal Liquid-Level Estimation for Closed-Loop Robotic Pouring

Abstract

We consider the problem of real-time liquid-level estimation for closed-loop robotic pouring. To this end, we propose a fast-slow architecture where a Vision-Language Model handles high-level task reasoning and a sensor-driven fast system provides low-latency feedback. As a first instantiation of the fast system, we present RadarEye, a mmWave radar signal processing pipeline that tracks liquid level during pouring. RadarEye combines (i) AoA–ToF beamforming for liquid surface localization with (ii) a physics-informed tracker that suppresses multipath interference. In real-robot experiments, RadarEye achieves 0.35 cm median error at 0.62 ms per-update latency, outperforming vision and ultrasound baselines.