Accurate Pouring with an Autonomous Robot Using an RGB-D Camera
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Abstract (arXiv)
Robotic assistants in a home environment are expected to perform various complex tasks for their users. One particularly challenging task is pouring drinks into cups, which for successful completion, requires the detection and tracking of the liquid level during a pour to determine when to stop. In this paper, we present a novel approach to autonomous pouring that tracks the liquid level using an RGB-D camera and adapts the rate of pouring based on the liquid level feedback. We thoroughly evaluate our system on various types of liquids and under different conditions, conducting over 250 pours with a PR2 robot. The results demonstrate that our approach is able to pour liquids to a target height with an accuracy of a few millimeters.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2018.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 0; 2024: 1.
8 papers.
- 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
- 2022Self-supervised Transparent Liquid Segmentation for Robotic Pouringcited by 9
- 2021The CORSMAL benchmark for the prediction of the properties of containerscited by 6
- 2021Manipulation of Granular Materials by Learning Particle Interactionscited by 6
- 2021Improving filling level classification with adversarial trainingcited by 5
- 2024Vision-based robot manipulation of transparent liquid containers in a laboratory settingcited by 2
- 2021Pouring Dynamics Estimation Using Gated Recurrent Unitscited by 1
- 2022Data augmentation with mixtures of max-entropy transformations for filling-level classificationcited by 1
- 2024Vision-based robot manipulation of transparent liquid containers in a laboratory setting25 Apr 2024
- 2022Data augmentation with mixtures of max-entropy transformations for filling-level classification8 Mar 2022
- 2022Self-supervised Transparent Liquid Segmentation for Robotic Pouring3 Mar 2022
- 2021Manipulation of Granular Materials by Learning Particle Interactions3 Nov 2021
- 2021The CORSMAL benchmark for the prediction of the properties of containers27 Jul 2021
- 2021Pouring Dynamics Estimation Using Gated Recurrent Units8 May 2021
- 2021Improving filling level classification with adversarial training8 Feb 2021
- 2021Semantics for Robotic Mapping, Perception and Interaction: A Survey2 Jan 2021