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Deep reinforcement learning has emerged as a popular and powerful way to develop locomotion controllers for quadruped robots.
M. H. Raibert, Legged robots that balance . MIT press, 1986
1986
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, Reinforcement learning - an introduction , ser. Adaptive computation and machine learning. MIT Press, 1998
1998
Earlier work this paper cites.
J. Buchli, E. Theodorou, F. Stulp, and S. Schaal, “Variable impedance control - a reinforcement learning approach,” in Proceedings of Robotics: Science and Systems , Zaragoza, Spain, June 2010
2010
Earlier work this paper cites.
F. Stulp, J. Buchli, A. Ellmer, M. Mistry, E. A. Theodorou, and S. Schaal, “Model-free reinforcement learning of impedance control in stochastic environments,” IEEE Transactions on Autonomous Mental Development , vol. 4, no. 4, pp. 330–341, 2012
2012
Earlier work this paper cites.
I. Mordatch, K. Lowrey, and E. Todorov, “Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 5307–5314
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
X. B. Peng and M. van de Panne, “Learning locomotion skills using deeprl: Does the choice of action space matter?” in Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation , ser. SCA ’17. Association for Computing Machinery, 2017
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “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) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim, “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, pp. 1–9
2018
Earlier work this paper cites.
C. D. Bellicoso, F. Jenelten, C. Gehring, and M. Hutter, “Dynamic locomotion through online nonlinear motion optimization for quadrupedal robots,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 2261–2268, 2018
2018
Earlier work this paper cites.
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke, “Sim-to-real: Learning agile locomotion for quadruped robots.” in Robotics: Science and Systems , 2018
2018
Earlier work this paper cites.
A. Iscen, K. Caluwaerts, J. Tan, T. Zhang, E. Coumans, V. Sindhwani, and V. Vanhoucke, “Policies modulating trajectory generators,” in Conference on Robot Learning . PMLR, 2018, pp. 916–926
2018
Earlier work this paper cites.
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 3803–3810
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
R. Martín-Martín, M. A. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg, “Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 1010–1017
2019
Cited alongside, same era.
J. Luo, E. Solowjow, C. Wen, J. A. Ojea, A. M. Agogino, A. Tamar, and P. Abbeel, “Reinforcement learning on variable impedance controller for high-precision robotic assembly,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 3080–3087
2019
Cited alongside, same era.
G. Bellegarda and K. Byl, “Training in task space to speed up and guide reinforcement learning,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 2693–2699
A. Kumar, Z. Fu, D. Pathak, and J. Malik, “RMA: Rapid Motor Adaptation for Legged Robots,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021
Closest in time.
Z. Fu, A. Kumar, J. Malik, and D. Pathak, “Minimizing energy consumption leads to the emergence of gaits in legged robots,” in 5th Annual Conference on Robot Learning , 2021
2021
Closest in time.
N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in 5th Annual Conference on Robot Learning , 2021
2021
Closest in time.
G. B. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. bae Kim, and P. Agrawal, “Learning to jump from pixels,” in 5th Annual Conference on Robot Learning , 2021
2021
Closest in time.
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2019
Cited alongside, same era.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org , 2016–2019
2019
Cited alongside, same era.
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Conference on Robot Learning . PMLR, 2020, pp. 317–329
2020
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, no. 47, 2020
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Siekmann, S. Valluri, J. Dao, F. Bermillo, H. Duan, A. Fern, and J. Hurst, “Learning Memory-Based Control for Human-Scale Bipedal Locomotion,” in Robotics: Science and Systems , 2020
2020
Cited alongside, same era.
M. Sombolestan, Y. Chen, and Q. Nguyen, “Adaptive force-based control for legged robots,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 7440–7447
2021
Cited alongside, same era.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 7309–7315
2021
Cited alongside, same era.
2021
Closest in time.
H. Duan, J. Dao, K. Green, T. Apgar, A. Fern, and J. Hurst, “Learning task space actions for bipedal locomotion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 1276–1282
2021
Closest in time.
I. Exarchos, Y. Jiang, W. Yu, and C. K. Liu, “Policy transfer via kinematic domain randomization and adaptation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 45–51
2021
Closest in time.
Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg, “Dynamics randomization revisited: A case study for quadrupedal locomotion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4955–4961
2021
Closest in time.
Unitree Robotics. (2021, February) A1. https://www.unitree.com/products/a1/
2021
Closest in time.
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
2022
Closest in time.
R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang, “Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers,” in International Conference on Learning Representations , 2022
2022
Closest in time.
W. Yu, D. Jain, A. Escontrela, A. Iscen, P. Xu, E. Coumans, S. Ha, J. Tan, and T. Zhang, “Visual-locomotion: Learning to walk on complex terrains with vision,” in Conference on Robot Learning . PMLR, 2022, pp. 1291–1302
2022
Closest in time.
Y. Shao, Y. Jin, X. Liu, W. He, H. Wang, and W. Yang, “Learning free gait transition for quadruped robots via phase-guided controller,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1230–1237, 2022
2022
Closest in time.
Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots, “Fast and efficient locomotion via learned gait transitions,” in Conference on Robot Learning . PMLR, 2022, pp. 773–783
2022
Closest in time.