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Learning Bipedal Walking on a Quadruped Robot via Adversarial Motion Priors (2407.02282v1)

Published 2 Jul 2024 in cs.RO

Abstract: Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robots remains unverified. This paper explores the adaptation of a learning framework originally designed for quadrupedal robots to operate blind locomotion in biped mode. We leverage a framework that incorporates Adversarial Motion Priors with a teacher-student policy to enable imitation of a reference trajectory and navigation on tough terrain. Our work involves transferring and evaluating a similar learning framework on a quadruped robot in biped mode, aiming to achieve stable walking on both flat and complicated terrains. Our simulation results demonstrate that the trained policy enables the quadruped robot to navigate both flat and challenging terrains, including stairs and uneven surfaces.

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References (23)
  1. L. Bao, J. Humphreys, T. Peng, and C. Zhou, “Deep reinforcement learning for bipedal locomotion: A brief survey,” 2024.
  2. J. Ho and S. Ermon, “Generative adversarial imitation learning,” in International Conference on Neural Information Processing Systems, 2016, pp. 4572–4580.
  3. X. Peng, G. Berseth, K. Yin, and M. Panne, “DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning,” ACM Transactions on Graphics, vol. 36, pp. 1–13, 2017.
  4. Z. Xie, H. Ling, N. Kim, and M. Panne, “ALLSTEPS: Curriculum-driven learning of stepping stone skills,” Computer Graphics Forum, vol. 39, pp. 213–224, 2020.
  5. J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in IEEE International Conference on Robotics and Automation, 2021, pp. 7309–7315.
  6. Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. van de Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Conference on Robot Learning, 2020, pp. 317–329.
  7. Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots,” in IEEE International Conference on Robotics and Automation, 2021, pp. 2811–2817.
  8. Q. Zhang, P. Cui, D. Yan, J. Sun, Y. Duan, A. Zhang, and R. Xu, “Whole-body humanoid robot locomotion with human reference,” arXiv preprint arXiv:2402.18294, 2024.
  9. J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 4975–4982, 2023.
  10. A. Escontrela, X. B. Peng, W. Yu, T. Zhang, A. Iscen, K. Goldberg, and P. Abbeel, “Adversarial motion priors make good substitutes for complex reward functions,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022, pp. 25–32.
  11. Y. Wang, Z. Jiang, and J. Chen, “Learning robust, agile, natural legged locomotion skills in the wild,” in RoboLetics: Workshop on Robot Learning in Athletics@ CoRL, 2023.
  12. A. Winkler, C. D. Bellicoso, M. Hutter, and J. Buchli, “Gait and trajectory optimization for legged systems through phase-based end-effector parameterization,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1560–1567, 2018.
  13. F. Yu, R. Batke, J. Dao, J. Hurst, K. Green, and A. Fern, “Dynamic bipedal turning through sim-to-real reinforcement learning,” in IEEE-RAS International Conference on Humanoid Robots, 2022, pp. 903–910.
  14. H. Duan, J. Dao, K. Green, T. Apgar, A. Fern, and J. Hurst, “Learning task space actions for bipedal locomotion,” in IEEE International Conference on Robotics and Automation, 2021, pp. 1276–1282.
  15. G. A. Castillo, B. Weng, S. Yang, W. Zhang, and A. Hereid, “Template model inspired task space learning for robust bipedal locomotion,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2023, pp. 8582–8589.
  16. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics, vol. 5, no. 47, p. eabc5986, 2020.
  17. A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034, 2021.
  18. C. Yu and A. Rosendo, “Multi-modal legged locomotion framework with automated residual reinforcement learning,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 312–10 319, 2022.
  19. X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “AMP: Adversarial motion priors for stylized physics-based character control,” ACM Transactions on Graphics, vol. 40, no. 4, pp. 1–20, 2021.
  20. A. W. Winkler, “TOWR–an open-source trajectory optimizer for legged robots in c,” 2018, [Online]. Available: https://github.com/ethz-adrl/towr.
  21. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
  22. G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” arXiv preprint arXiv:2205.02824, 2022.
  23. N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning.   PMLR, 2022, pp. 91–100.
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Authors (6)
  1. Tianhu Peng (4 papers)
  2. Lingfan Bao (6 papers)
  3. Joseph Humphreys (3 papers)
  4. Andromachi Maria Delfaki (2 papers)
  5. Dimitrios Kanoulas (33 papers)
  6. Chengxu Zhou (9 papers)
Citations (1)
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