# Visual Closed-Loop Control for Pouring Liquids

arXiv: https://arxiv.org/abs/1610.02610 · HTML: https://mlsanity.com/paper/1610.02610/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2016.

## Built on by (most cited first, 26 of 26)

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- [Continuously Improving Mobile Manipulation with Autonomous Real-World RL](https://mlsanity.com/paper/2409.20568/) (2024)
- [Privileged Sensing Scaffolds Reinforcement Learning](https://mlsanity.com/paper/2405.14853/) (2024)
- [Explainable Hierarchical Imitation Learning for Robotic Drink Pouring](https://mlsanity.com/paper/2105.07348/) (2021)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation](https://mlsanity.com/paper/2405.18688/) (2024)
- [Manipulation of Granular Materials by Learning Particle Interactions](https://mlsanity.com/paper/2111.02274/) (2021)
- [One-Shot Domain-Adaptive Imitation Learning via Progressive Learning](https://mlsanity.com/paper/2204.11251/) (2022)
- [DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics](https://mlsanity.com/paper/2312.06408/) (2023)
- [Improving filling level classification with adversarial training](https://mlsanity.com/paper/2102.04057/) (2021)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)
- [Image Based Reconstruction of Liquids from 2D Surface Detections](https://mlsanity.com/paper/2111.11491/) (2021)
- [Vision-based robot manipulation of transparent liquid containers in a laboratory setting](https://mlsanity.com/paper/2404.16529/) (2024)
- [Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations](https://mlsanity.com/paper/2107.00722/) (2021)
- [Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Grasping](https://mlsanity.com/paper/2202.11503/) (2022)
- [Data augmentation with mixtures of max-entropy transformations for filling-level classification](https://mlsanity.com/paper/2203.04027/) (2022)
- [Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning](https://mlsanity.com/paper/2212.08961/) (2022)
- [Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction](https://mlsanity.com/paper/2308.02715/) (2023)
- [Trajectory-Oriented Policy Optimization with Sparse Rewards](https://mlsanity.com/paper/2401.02225/) (2024)
- [SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policy](https://mlsanity.com/paper/2510.11566/) (2025)
- [ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Process](https://mlsanity.com/paper/2512.01268/) (2025)

## Abstract (arXiv)

Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.