# Reasoning About Liquids via Closed-Loop Simulation

arXiv: https://arxiv.org/abs/1703.01656 · HTML: https://mlsanity.com/paper/1703.01656/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [3D Neural Scene Representations for Visuomotor Control](https://mlsanity.com/paper/2107.04004/) (2021)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids](https://mlsanity.com/paper/2203.05775/) (2022)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [Improving filling level classification with adversarial training](https://mlsanity.com/paper/2102.04057/) (2021)
- [Compact Task-Aligned Imitation Learning for Laboratory Automation](https://mlsanity.com/paper/2603.01110/) (2026)

## Abstract (arXiv)

Simulators are powerful tools for reasoning about a robot's interactions with its environment. However, when simulations diverge from reality, that reasoning becomes less useful. In this paper, we show how to close the loop between liquid simulation and real-time perception. We use observations of liquids to correct errors when tracking the liquid's state in a simulator. Our results show that closed-loop simulation is an effective way to prevent large divergence between the simulated and real liquid states. As a direct consequence of this, our method can enable reasoning about liquids that would otherwise be infeasible due to large divergences, such as reasoning about occluded liquid.