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2024

PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots

Luo, Shixin, Li, Songbo, Yu, Ruiqi et al.

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Parkour presents a highly challenging task for legged robots, requiring them to traverse various terrains with agile and smooth locomotion.

This necessitates comprehensive understanding of both the robot's own state and the surrounding terrain, despite the inherent unreliability of robot perception and actuation.
Current state-of-the-art methods either rely on complex pre-trained high-level terrain reconstruction modules or limit the maximum potential of robot parkour to avoid failure due to inaccurate perception.
In this paper, we propose a one-stage end-to-end learning-based parkour framework: Parkour with Implicit-Explicit learning framework for legged robots (PIE) that leverages dual-level implicit-explicit estimation.

PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots · Around

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G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
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T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
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Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” arXiv preprint arXiv:2309.05665 , 2023
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Then

R. Yang, G. Yang, and X. Wang, “Neural volumetric memory for visual locomotion control,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1430–1440
2023
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J. Long, Z. Wang, Q. Li, J. Gao, L. Cao, and J. Pang, “Hybrid internal model: A simple and efficient learner for agile legged locomotion,” arXiv preprint arXiv:2312.11460 , 2023
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W. Wang, D. Han, X. Luo, and D. Li, “Addressing signal delay in deep reinforcement learning,” in The Twelfth International Conference on Learning Representations , 2023
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D. Hoeller, N. Rudin, D. Sako, and M. Hutter, “Anymal parkour: Learning agile navigation for quadrupedal robots,” Science Robotics , vol. 9, no. 88, p. eadi7566, 2024
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