Adriaens, LouisLouisAdriaensLips, ThomasThomasLipsDe Coster, MathieuMathieuDe CosterVerleysen, AndreasAndreasVerleysenWyffels, FrancisFrancisWyffels2026-04-152026-04-1520252377-3766https://imec-publications.be/handle/20.500.12860/59091Robotic perception of transparent objects presents unique challenges due to their refractive properties, lack of texture, and limitations of conventional RGB-D sensors in capturing reliable depth information. These challenges significantly hinder robotic manipulation capabilities in real-world settings such as household assistance, hospitality, and healthcare. To address these issues, we propose SPILL: A lightweight perception pipeline for Size, Pose, and Internal Liquid Level estimation of unknown transparent glassware using a single view. SPILL combines object detection with semantic keypoint detection, and operates without requiring object-specific 3D models or depth completion. We demonstrate its effectiveness in autonomous robotic pouring tasks. Additionally, to enhance the robustness and generalization of keypoint detection to diverse real-world scenarios, we introduce Glasses-in-the-Wild, a new dataset that captures a wide variety of glass types in realistic environments. Evaluated on a robot manipulator, SPILL achieves a 93.6% success rate across 500 autonomous pours with 20 unseen glasses in three diverse real-world scenes. We further demonstrate robustness through multiple live public events in real-world, human-centered environments. In one recorded session, the robot autonomously served 62 drinks with a 98.3% success rate. These results demonstrate that task-relevant keypoint detection enables scalable, real-world transparent object interaction, paving the way for practical applications in service and assistive robotics - without spilling a drop.engJournal article10.1109/lra.2025.3625515WOS:001616261500005