Abstract
Surface grading is an integral part of the construction pipeline. Here, a dozer, which is a key machinery tool in any construction site, is required to level an uneven area containing pre-dumped sand piles. In this work, we aim to tackle the problem of autonomous surface grading on real-world scenarios. We design both a realistic physical simulation and a scaled real-world 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. However, we show that the simulation can be leveraged to guide a learning agent, which can generalize and solve the task both in simulation and in a scaled prototype environment.
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References
Caterpillar: D6/D6 XE track-type tractors specifications. https://s7d2.scene7.com/is/content/Caterpillar/CM20181217-51568-10948
Cundall, P.A., Strack, O.D.L.: A discrete numerical model for granular assemblies. Géotechnique 29(1), 47–65 (1979)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hirayama, M., Guivant, J., Katupitiya, J., Whitty, M.: Path planning for autonomous bulldozers. Mechatronics 58, 20–38 (2019)
Kim, W., Pavlov, C., Johnson, A.M.: Developing a simple model for sand-tool interaction and autonomously shaping sand. arXiv:1908.02745 (2019)
Kurinov, I., Orzechowski, G., Hämäläinen, P., Mikkola, A.: Automated excavator based on reinforcement learning and multibody system dynamics. IEEE Access 8, 213998–214006 (2020)
Loquercio, A., Kaufmann, E., Ranftl, R., Müller, M., Koltun, V., Scaramuzza, D.: Learning high-speed flight in the wild. Sci. Robot. 6(59), eabg5810 (2021)
Miron, Y., Coscas, Y.: S-flow GAN (2019)
Peng, X.B., Andrychowicz, M., Zaremba, W., Abbeel, P.: Sim-to-real transfer of robotic control with dynamics randomization. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3803–3810. IEEE (2018)
Pinto, L., Davidson, J., Sukthankar, R., Gupta, A.: Robust adversarial reinforcement learning. In: International Conference on Machine Learning, pp. 2817–2826. PMLR (2017)
Rao, K., Harris, C., Irpan, A., Levine, S., Ibarz, J., Khansari, M.: RL-CycleGAN: reinforcement learning aware simulation-to-real. In: Proceedings of the IEEE/CVF CVPR, pp. 11157–11166 (2020)
Reece, A.R.: Introduction to terrain vehicle systems: M. G. Bekker, Univ. of Michigan Press, Ann Arbor (1969). \$27.50. J. Terramech. 7, 75–77 (1970)
Romero-Ramirez, F.J., Muñoz-Salinas, R., Medina-Carnicer, R.: Speeded up detection of squared fiducial markers. Image Vis. Comput. 76, 38–47 (2018)
Ross, C., Miron, Y., Goldfracht, Y., Di Castro, D.: AGPNet-autonomous grading policy network. arXiv preprint arXiv:2112.10877 (2021)
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., Battaglia, P.: Learning to simulate complex physics with graph networks. In: International Conference on Machine Learning, pp. 8459–8468. PMLR (2020)
Stentz, A., Bares, J., Singh, S., Rowe, P.: A robotic excavator for autonomous truck loading. Auton. Robot. 7(2), 175–186 (1999)
Sulsky, D., Chen, Z., Schreyer, H.: A particle method for history-dependent materials. Comput. Methods Appl. Mech. Eng. 118(1), 179–196 (1994)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. MIT press (2018)
Tessler, C., Efroni, Y., Mannor, S.: Action robust reinforcement learning and applications in continuous control. In: ICML, pp. 6215–6224. PMLR (2019)
Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22(5–6), 544–557 (2009)
Wiesemann, W., Kuhn, D., Rustem, B.: Robust Markov decision processes. Math. Oper. Res. 38(1), 1–208 (2013)
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Miron, Y., Goldfracht, Y., Castro, D.D. (2023). Towards Autonomous Grading in the Real World. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_8
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