# Learning to Pour

arXiv: https://arxiv.org/abs/1705.09021 · HTML: https://mlsanity.com/paper/1705.09021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2017.

## Built on by (most cited first, 4 of 4)

- [From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network](https://mlsanity.com/paper/2309.09181/) (2023)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)
- [Pouring Dynamics Estimation Using Gated Recurrent Units](https://mlsanity.com/paper/2105.12828/) (2021)
- [Classifying States of Cooking Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2105.14196/) (2021)

## Abstract (arXiv)

Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics.