Fetching the paper…
Reading the bibliography…
The efficacy of reinforcement learning for robot control relies on the tailored integration of task-specific priors and heuristics for effective exploration, which challenges their straightforward application to complex tasks and necessitates a unified approach.
R. M. Alexander, “The gaits of bipedal and quadrupedal animals,” IJRR
1984
Earlier work this paper cites.
M. H. Raibert, “Legged robots,” Communications of the ACM
1986
Earlier work this paper cites.
E. Asarin, O. Bournez, T. Dang, O. Maler, and A. Pnueli, “Effective synthesis of switching controllers for linear systems,” Proceedings of the IEEE
2000
Earlier work this paper cites.
T. Koo, G. Pappas, and S. Sastry, “Multi-modal control of systems with constraints,” in Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)
2001
Earlier work this paper cites.
S. Levine and V. Koltun, “Guided policy search,” in Proceedings of the 30th International Conference on Machine Learning
2013
Earlier work this paper cites.
S. Levine and V. Koltun, “Learning complex neural network policies with trajectory optimization,” in Proceedings of the 31st International Conference on Machine Learning
2014
Earlier work this paper cites.
I. Mordatch and E. Todorov, “Combining the benefits of function approximation and trajectory optimization,” in Proceedings of Robotics: Science and Systems
2014
Earlier work this paper cites.
X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Trans. Graph
2018
Earlier work this paper cites.
R. Cheng, A. Verma, G. Orosz, S. Chaudhuri, Y. Yue, and J. Burdick, “Control regularization for reduced variance reinforcement learning,” in Proceedings of the 36th International Conference on Machine Learning
2019
Earlier work this paper cites.
J. Carius, F. Farshidian, and M. Hutter, “Mpc-net: A first principles guided policy search,” IEEE Robotics and Automation Letters
2020
Earlier work this paper cites.
S. Gangapurwala, A. Mitchell, and I. Havoutis, “Guided constrained policy optimization for dynamic quadrupedal robot locomotion,” IEEE Robotics and Automation Letters
2020
Cited alongside, same era.
L. Hasenclever, F. Pardo, R. Hadsell, N. Heess, and J. Merel, “CoMic: Complementary task learning &; mimicry for reusable skills,” in ICML
2020
Cited alongside, same era.
J. Norby and A. M. Johnson, “Fast global motion planning for dynamic legged robots,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2020
Cited alongside, same era.
J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst, “Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning,” in RSS
2021
Cited alongside, same era.
X. B. Peng, Z. Ma, P. Abbeel, S. Levine, and A. Kanazawa, “Amp: adversarial motion priors for stylized physics-based character control,” ACM Trans. Graph
X. Cheng, K. Shi, A. Agarwal, and D. Pathak, “Extreme parkour with legged robots,” in RoboLetics: Workshop @CoRL 2023
2023
Later among the works it cites.
Z. Zhuang, Z. Fu, J. Wang, C. G. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” in CoRL
2023
Later among the works it cites.
E. Vollenweider, M. Bjelonic, V. Klemm, N. Rudin, J. Lee, and M. Hutter, “Advanced skills through multiple adversarial motion priors in reinforcement learning,” in ICRA 2023
2023
Later among the works it cites.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” 2023
2023
Later among the works it cites.
L. Krishna and Q. Nguyen, “Learning multimodal bipedal locomotion and implicit transitions: A versatile policy approach,” in IEEE IROS
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
J. Li and Q. Nguyen, “Force-and-moment-based model predictive control for achieving highly dynamic locomotion on bipedal robots,” in IEEE CDC
2021
Cited alongside, same era.
N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter, “Advanced skills by learning locomotion and local navigation end-to-end,” in IEEE IROS
2022
Cited alongside, same era.
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
2022
Cited alongside, same era.
2022
Cited alongside, same era.
M. Murooka, M. Morisawa, and F. Kanehiro, “Centroidal trajectory generation and stabilization based on preview control for humanoid multi-contact motion,” IEEE RAL
2022
Cited alongside, same era.
D. Hoeller, N. Rudin, D. Sako, and M. Hutter, “Anymal parkour: Learning agile navigation for quadrupedal robots,” Science Robotics
2024
Closest in time.
F. Jenelten, J. He, F. Farshidian, and M. Hutter, “Dtc: Deep tracking control,” Science Robotics
2024
Closest in time.
Z. Li, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control,” 2024
2024
Closest in time.
Z. Luo, J. Wang, K. Liu, H. Zhang, C. Tessler, J. Wang, Y. Yuan, J. Cao, Z. Lin, F. Wang, J. Hodgins, and K. Kitani, “Smplolympics: Sports environments for physically simulated humanoids,” 2024
2024
Closest in time.
T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, J. Humplik, M. Wulfmeier, S. Tunyasuvunakool, N. Y. Siegel, R. Hafner, M. Bloesch, K. Hartikainen, A. Byravan, L. Hasenclever, Y. Tassa, F. Sadeghi, N. Batchelor, F. Casarini, S. Saliceti, C. Game, N. Sreendra, K. Patel, M. Gwira, A. Huber, N. Hurley, F. Nori, R. Hadsell, and N. Heess, “Learning agile soccer skills for a bipedal robot with deep reinforcement learning,” Science Robotics
2024
Closest in time.