Examining the simulation-to-reality gap of a wheel loader digging in deformable terrain
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Abstract (arXiv)
We investigate how well a physics-based simulator can replicate a real wheel loader performing bucket filling in a pile of soil. The comparison is made using field test time series of the vehicle motion and actuation forces, loaded mass, and total work. The vehicle was modeled as a rigid multibody system with frictional contacts, driveline, and linear actuators. For the soil, we tested discrete element models of different resolutions, with and without multiscale acceleration. The spatio-temporal resolution ranged between 50-400 mm and 2-500 ms, and the computational speed was between 1/10,000 to 5 times faster than real-time. The simulation-to-reality gap was found to be around 10% and exhibited a weak dependence on the level of fidelity, e.g., compatible with real-time simulation. Furthermore, the sensitivity of an optimized force feedback controller under transfer between different simulation domains was investigated. The domain bias was observed to cause a performance reduction of 5% despite the domain gap being about 15%.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2023.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Control Synthesis with Reinforcement Learning: A Modeling Perspectivecited by 1
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- 2026Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulationcited by 0
- 2026Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation11 Sep 2026
- 2025Control Synthesis with Reinforcement Learning: A Modeling Perspective29 Oct 2025
- 2025An integrated process for design and control of lunar robotics using AI and simulation15 Sep 2025