# Inferring the Material Properties of Granular Media for Robotic Tasks

arXiv: https://arxiv.org/abs/2003.08032 · HTML: https://mlsanity.com/paper/2003.08032/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [Dynamic-Resolution Model Learning for Object Pile Manipulation](https://mlsanity.com/paper/2306.16700/) (2023)
- [Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation](https://mlsanity.com/paper/2109.08815/) (2021)
- [A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation](https://mlsanity.com/paper/2112.05068/) (2021)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization](https://mlsanity.com/paper/2207.14561/) (2022)
- [Manipulation of Granular Materials by Learning Particle Interactions](https://mlsanity.com/paper/2111.02274/) (2021)
- [Understanding Physical Effects for Effective Tool-use](https://mlsanity.com/paper/2206.14998/) (2022)
- [DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting](https://mlsanity.com/paper/2203.10263/) (2022)
- [Tactile-based Object Retrieval From Granular Media](https://mlsanity.com/paper/2402.04536/) (2024)
- [Learning to Simulate Tree-Branch Dynamics for Manipulation](https://mlsanity.com/paper/2306.03410/) (2023)
- [BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym](https://mlsanity.com/paper/2107.04527/) (2021)
- [Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning](https://mlsanity.com/paper/2410.04719/) (2024)
- [Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking](https://mlsanity.com/paper/2410.14005/) (2024)
- [A Distributional Treatment of Real2Sim2Real for Object-Centric Agent Adaptation in Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2502.18615/) (2025)
- [Towards scientific machine learning for granular material simulations -- challenges and opportunities](https://mlsanity.com/paper/2504.08766/) (2025)
- [Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2510.26656/) (2025)
- [Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation](https://mlsanity.com/paper/2609.12677/) (2026)

## Abstract (arXiv)

Granular media (e.g., cereal grains, plastic resin pellets, and pills) are ubiquitous in robotics-integrated industries, such as agriculture, manufacturing, and pharmaceutical development. This prevalence mandates the accurate and efficient simulation of these materials. This work presents a software and hardware framework that automatically calibrates a fast physics simulator to accurately simulate granular materials by inferring material properties from real-world depth images of granular formations (i.e., piles and rings). Specifically, coefficients of sliding friction, rolling friction, and restitution of grains are estimated from summary statistics of grain formations using likelihood-free Bayesian inference. The calibrated simulator accurately predicts unseen granular formations in both simulation and experiment; furthermore, simulator predictions are shown to generalize to more complex tasks, including using a robot to pour grains into a bowl, as well as to create a desired pattern of piles and rings. Visualizations of the framework and experiments can be viewed at https://youtu.be/OBvV5h2NMKA