Towards Autonomous Grading In The Real World
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Abstract (arXiv)
In this work, we aim to tackle the problem of autonomous grading, where a dozer is required to flatten an uneven area. In addition, we explore methods for bridging the gap between a simulated environment and real scenarios. We design both a realistic physical simulation and a scaled real prototype environment mimicking the real dozer dynamics and sensory information. We establish heuristics and learning strategies in order to solve the problem. Through extensive experimentation, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real world scenarios. As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 13 Jun 2022.
Builds on
10 of 34 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
- 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
- 2017Robust Adversarial Reinforcement Learningcited by 451
- 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
- 2021Learning High-Speed Flight in the Wildcited by 128
- 2020RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Realcited by 91
- 2019Developing a Simple Model for Sand-Tool Interaction and Autonomously Shaping Sandcited by 3
- 2019S-Flow GANcited by 1
- 2021AGPNet -- Autonomous Grading Policy Networkcited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.