@article{01K8DAZQMF08N0NXVXXHJF49F0,
  abstract     = {{Robotic 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.}},
  author       = {{Adriaens, Louis and Lips, Thomas and De Coster, Mathieu and Verleysen, Andreas and wyffels, Francis}},
  issn         = {{2377-3766}},
  journal      = {{IEEE ROBOTICS AND AUTOMATION LETTERS}},
  keywords     = {{Robots,Glass,Liquids,Solid modeling,Three-dimensional displays,Cameras,Semantics,Pipelines,Computational modeling,Robustness,Perception for Grasping and Manipulation,Object Detection,Segmentation and Categorization,RGB-D perception,Data Sets for Robotic Vision,Service robotics}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{13288--13295}},
  title        = {{SPILL : size, pose, and internal liquid level estimation of transparent glassware for robotic bartending}},
  url          = {{http://doi.org/10.1109/LRA.2025.3625515}},
  volume       = {{10}},
  year         = {{2025}},
}

Altmetric

View in Altmetric

Web of Science

Times cited: