# World Modeling for Autonomous Wheel Loaders

arXiv: https://arxiv.org/abs/2309.12016 · HTML: https://mlsanity.com/paper/2309.12016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 0 of its 29 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Built on by (most cited first, 1 of 1)

- [An integrated process for design and control of lunar robotics using AI and simulation](https://mlsanity.com/paper/2509.12367/) (2025)

## Abstract (arXiv)

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.