# Learning Robotic Manipulation of Granular Media

arXiv: https://arxiv.org/abs/1709.02833 · HTML: https://mlsanity.com/paper/1709.02833/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Dynamic-Resolution Model Learning for Object Pile Manipulation](https://mlsanity.com/paper/2306.16700/) (2023)
- [How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning](https://mlsanity.com/paper/2106.02445/) (2021)
- [Manipulation of Granular Materials by Learning Particle Interactions](https://mlsanity.com/paper/2111.02274/) (2021)
- [Learning to Manipulate Amorphous Materials](https://mlsanity.com/paper/2103.02533/) (2021)
- [GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.05193/) (2023)
- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [Interactive Shaping of Granular Media Using Reinforcement Learning](https://mlsanity.com/paper/2509.06469/) (2025)
- [SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policy](https://mlsanity.com/paper/2510.11566/) (2025)
- [Few-shot Scooping Under Domain Shift via Simulated Maximal Deployment Gaps](https://mlsanity.com/paper/2408.02949/) (2024)

## Abstract (arXiv)

In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions. These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape. Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media. We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must otherwise implicitly predict the same mechanics in order to produce accurate value estimates.