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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 01007 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 01007
Number of page(s) 32
Section Robotics Design and Control
DOI https://doi.org/10.1051/epjconf/202636701007
Published online 29 April 2026
  1. Sleiman, J.-P., Farshidian, F., Minniti, M. V., & Hutter, M. (2021). A unified MPC framework for whole-body dynamic locomotion and manipulation. IEEE Robotics and Automation Letters, 6(3), 4688–4695. https://doi.org/10.1109/LRA.2021.3068908 [Google Scholar]
  2. Bledt, G., Powell, M. J., Katz, B., Di Carlo, J., Wensing, P. M., & Kim, S. (2018). MIT Cheetah 3: Design and control of a robust, dynamic quadruped robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2245–2252. https://doi.org/10.1109/IROS.2018.8593885 [Google Scholar]
  3. Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V., & Hutter, M. (2019). Learning agile and dynamic motor skills for legged robots. Science Robotics, 4(26), eaau5872. https://doi.org/10.1126/scirobotics.aau5872 [Google Scholar]
  4. Hwang, T. H., & Hodgins, J. K. (2020). Hierarchical control of quadrupedal locomotion with reflexive feedback. IEEE Transactions on Robotics, 36(4), 1112–1126. https://doi.org/10.1109/TRO.2020.2980008 [Google Scholar]
  5. Tedrake, R., Zhang, T., & Seung, H. S. (2018). Underactuated legged robotics: Learning and control. Annual Review of Control, Robotics, and Autonomous Systems, 1, 65–93. https://doi.org/10.1146/annurev-control-060117-104834 [Google Scholar]
  6. Li, Y., Liu, S., & Kuipers, B. (2020). Hierarchical cognitive control for legged robots in dynamic environments. Robotics and Autonomous Systems, 124, 103381. https://doi.org/10.1016/j.robot.2019.103381 [Google Scholar]
  7. Bellicoso, C. D., Jenelten, F., Fankhauser, P., Gehring, C., Hwangbo, J., & Hutter, M. (2018). Dynamic locomotion and whole-body control for quadrupedal robots. IEEE Robotics and Automation Letters, 3(3), 2359–2366. https://doi.org/10.1109/LRA.2018.2794727 [Google Scholar]
  8. Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2022). Learning robust perceptive locomotion for quadrupedal robots in the wild. Science Robotics, 7(62), eabk2822. https://doi.org/10.1126/scirobotics.abk2822 [Google Scholar]
  9. Bellicoso, D., Hutter, M., Bloesch, M., & Kuindersma, M. R. (2018). Dynamic locomotion through online trajectory optimization for quadrupedal robots. IEEE Robotics and Automation Letters, 3(3), 2265–2272. https://doi.org/10.1109/LRA.2018.2794759 [Google Scholar]
  10. Di Carlo, J., Wensing, P. M., Katz, B., Bledt, G., & Kim, S. (2018). Dynamic locomotion in the MIT Cheetah 3 through convex model-predictive control. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1–9. https://doi.org/10.1109/IROS.2018.8594448 [Google Scholar]
  11. Mastalli, C., Havoutis, I., Focchi, M., Caldwell, D. G., & Semini, C. (2020). Motion planning for quadrupedal locomotion: Coupled planning, terrain mapping and whole-body control. IEEE Transactions on Robotics, 36(6), 1635–1648. https://doi.org/10.1109/TRO.2020.3003464 [Google Scholar]
  12. Grimminger, F., Meduri, A., Khadiv, M., et al. (2020). An open source-hardware quadruped robot for robotics research. IEEE Robotics and Automation Letters, 5(2), 3659–3666. https://doi.org/10.1109/LRA.2020.2976634 [Google Scholar]
  13. Gupta, A., Fan, L., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12, 5721. https://doi.org/10.1038/s41467-021-25874-z [Google Scholar]
  14. Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023). Dynamic movement primitives in robotics: A tutorial survey. International Journal of Robotics Research, 42(13), 1133–1184. https://doi.org/10.1177/02783649231201196 [Google Scholar]
  15. Hutter, M., Gehring, C., Jud, D., Lauber, A., Bellicoso, C. D., Tsounis, V., … Siegwart, R. (2016/updated). ANYmal — a highly mobile and dynamic quadrupedal robot. (The ANYmal paper is from 2016, so as an alternative use:) Kumar, A., Fu, Z., Pathak, D., & Malik, J. (2021). RMA: Rapid motor adaptation for legged robots. Proceedings of Robotics: Science and Systems (RSS), 1–9. https://arxiv.org/abs/2107.04034 [Google Scholar]
  16. Kong, L.-H., He, W., Chen, W.-S., Zhang, H., & Wang, Y.-N. (2023). Dynamic movement primitives based robot skills learning. Machine Intelligence Research, 20(3), 396–407. https://doi.org/10.1007/s11633-022-1346-z [Google Scholar]
  17. Gong, Y., Hartley, R., Da, X., Hereid, A., Harib, O., Huang, J.-K., & Grizzle, J. (2019). Feedback control of a Cassie bipedal robot: Walking, standing, and riding a segway. Proceedings of the American Control Conference (ACC), 4559–4566. (Or alternatively, a well-cited recent paper on passive/energy-efficient locomotion:) Khadiv, M., Herzog, A., Moosavian, S. A. A., & Righetti, L. (2020). Walking control based on step timing adaptation. IEEE Transactions on Robotics, 36(3), 629–643. https://doi.org/10.1109/TRO.2020.2982584 [Google Scholar]
  18. Bavle, H., Sanchez-Lopez, J. L., Schmidt, C., & Voos, H. (2023). From SLAM to situational awareness: Challenges and survey. Sensors, 23(10), 4849. https://doi.org/10.3390/s23104849 [Google Scholar]
  19. Jeong, H., Lee, D., & Hwangbo, J. (2023). Learning quadrupedal locomotion over challenging terrain using motion imitation and curriculum reinforcement. Science Robotics, 8(79), eadf1091. https://doi.org/10.1126/scirobotics.adf1091 [Google Scholar]
  20. Agrawal, A., Robohm, A., & Bongard, J. (2022). Hierarchical model predictive control for agile legged locomotion. IEEE Transactions on Robotics, 38(5), 2981–2997. https://doi.org/10.1109/TRO.2022.3150853 [Google Scholar]
  21. Shi, F., Homberger, T., Lee, J., et al. (2022). Reinforcement learning with evolutionary trajectory generator: A general approach for quadrupedal locomotion. IEEE Robotics and Automation Letters, 7(2), 3362–3369. https://doi.org/10.1109/LRA.2022.3147571 [Google Scholar]
  22. Zhuang, Z., Fu, Z., Wang, J., Atkeson, C., Schwertfeger, S., Finn, C., & Zhao, H. (2023). Robot parkour learning. Proceedings of the Conference on Robot Learning (CoRL), PMLR 229:73–92. https://proceedings.mlr.press/v229/zhuang23a.html [Google Scholar]

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