Abstract
Learning diverse and flexible locomotion strategies in uncertain environments has been a longstanding challenge for quadruped robots. Although recent progress in domain randomization has partially tackled this difficulty by training policies on a wide range of potential factors, there is still a great need for improving efficiency. In this paper, we propose a novel framework for adaptive quadruped robot locomotion learning in uncertain environments. Our method is based on data-efficient reinforcement learning and learns simulation parameters iteratively. We also propose a novel Sampling-Interval-Adaptive Identification (SIAI) strategy that uses historical parameters to optimize sampling distribution and then improve identification accuracy. Final evaluations based on multiple robotic locomotion tasks showed superiority of our method over baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raibert, M.H., Tello, E.R.: Legged robots that balance. IEEE Expert (1986)
Katz, B., Carlo, J.D., Kim, S.: Mini cheetah: a platform for pushing the limits of dynamic quadruped control. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)
Carlo, J.D., Wensing, P.M., Katz, B., Bledt, G., Kim, S.: Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018)
Ding, Y., Pandala, A., Li, C., Shin, Y.H., Park, H.W.: Representation-free model predictive control for dynamic motions in quadrupeds. IEEE Trans. Robot. (2020)
Matas, J., James, S., Davison, A.J.: Sim-to-real reinforcement learning for deformable object manipulation. In: Conference on Robot Learning (2018)
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., Hutter, M.: Learning quadrupedal locomotion over challenging terrain. Sci. Robot. (2020)
Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., Hutter, M.: Learning robust perceptive locomotion for quadrupedal robots in the wild. Sci. Robot. (2022)
Yang, Y., Caluwaerts, K., Iscen, A., Zhang, T., Tan, J., Sindhwani, V.: Data efficient reinforcement learning for legged robots. In: Conference on Robot Learning (2020)
Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., Levine, S.: Learning to walk via deep reinforcement learning. Robot. Sci. Syst. (2019)
Tan, J., Zhang, T., Coumans, E., et al.: Sim-to-real: Learning agile locomotion for quadruped robots. Robot. Sci. Syst. (2018)
Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: Advances in Artificial Life: Third European Conference on Artificial Life Granada, Spain, 4–6 June 1995, Proceedings, vol. 3 (1995)
Koos, S., Mouret, J.-B., Doncieux, S.: Crossing the reality gap in evolutionary robotics by promoting transferable controllers. in: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (2010)
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., Abbeel, P.: Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)
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) (2018)
Farchy, A., Barrett, S., MacAlpine, P., Stone, P.: Humanoid robots learning to walk faster: from the real world to simulation and back. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems (2013)
Tan, J., Xie, Z., Boots, B., Liu, C.K.: Simulation-based design of dynamic controllers for humanoid balancing. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016)
Du, Y., Watkins, O., Darrell, T., Abbeel, P., Pathak, D.: Auto-tuned sim-to-real transfer. In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021)
Chebotar, Y., Handa, A., Makoviychuk, V., et al.: Closing the sim-to-real loop: adapting simulation randomization with real-world experience. In: 2019 International Conference on Robotics and Automation (ICRA) (2019)
Mastalli, C., Havoutis, I., Focchi, M., Caldwell, D.G., Semini, C.: Motion planning for quadrupedal locomotion: coupled planning, terrain mapping, and whole-body control. IEEE Trans. Robot. (2020)
Rudin, N., Hoeller, D., Reist, P., Hutter, M.: Learning to walk in minutes using massively parallel deep reinforcement learning. In: Conference on Robot Learning (2022)
Sorokin, M., Tan, J., Liu, C.K., Ha, S.: Learning to navigate sidewalks in outdoor environments. IEEE Robot. Autom. Lett. (2022)
Agarwal, A., Kumar, A., Malik, J., Pathak, D.: Legged locomotion in challenging terrains using egocentric vision. In: 6th Annual Conference on Robot Learning (2022)
Tsounis, V., Alge, M., Lee, J., Farshidian, F., Hutter, M.: Deepgait: planning and control of quadrupedal gaits using deep reinforcement learning. IEEE Robot. Autom. Lett. (2020)
Smith, L., Kew, J.C., Peng, X.B., Ha, S., Tan, J., Levine, S.: Legged robots that keep on learning: fine-tuning locomotion policies in the real world. In: 2022 International Conference on Robotics and Automation (ICRA) (2022)
Peng, X.B., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., Levine, S.: Learning agile robotic locomotion skills by imitating animals. arXiv preprint arXiv:2004.00784 (2020)
Nagabandi, A., Clavera, I., Liu, S., et al.: Learning to adapt in dynamic, real-world environments through meta-reinforcement learning. In: International Conference on Learning Representations (2018)
Yu, W., Tan, J., Liu, C.K., Turk, G.: Preparing for the unknown: learning a universal policy with online system identification. Robot. Sci. Syst. (2017)
Zhu, S., Kimmel, A., Bekris, K., Boularias, A.: Fast model identification via physics engines for data-efficient policy search. In: International Joint Conference on Artificial Intelligence (IJCAI) (2018)
Hansen, N.: The CMA evolution strategy: a tutorial. arXiv preprint arXiv:1604.00772 (2016)
Jiang, Y., Zhang, T., Ho, D., et al.: SimGAN: hybrid simulator identification for domain adaptation via adversarial reinforcement learning. In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (2021)
Allevato, A., Short, E.S., Pryor, M., Thomaz, A.: Tunenet: one-shot residual tuning for system identification and sim-to-real robot task transfer. In: Conference on Robot Learning (2020)
Iscen, A., Caluwaerts, K., Tan, J., et al.: Policies modulating trajectory generators. In: Conference on Robot Learning (2018)
Coumans, E., Bai, Y.: Pybullet, a Python module for physics simulation for games, robotics and machine learning (2016). http://pybullet.org
Wang, X.: Unitree robotics. https://www.unitree.com/
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Acknowledgements
This work was partially supported by the National Science Fund for Distinguished Young Scholars (62025205), National Natural Science Foundation of China (62032020, 62102317), and the Huawei-NPU Collaboration Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, M. et al. (2024). A Novel Framework for Adaptive Quadruped Robot Locomotion Learning in Uncertain Environments. In: Jin, H., Yu, Z., Yu, C., Zhou, X., Lu, Z., Song, X. (eds) Green, Pervasive, and Cloud Computing. GPC 2023. Lecture Notes in Computer Science, vol 14504. Springer, Singapore. https://doi.org/10.1007/978-981-99-9896-8_10
Download citation
DOI: https://doi.org/10.1007/978-981-99-9896-8_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9895-1
Online ISBN: 978-981-99-9896-8
eBook Packages: Computer ScienceComputer Science (R0)