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Several earlier studies have shown impressive control performance in complex robotic systems by designing the controller using a neural network and training it with model-free reinforcement learning.
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J. Garcıa and F. Fernández, “A comprehensive survey on safe reinforcement learning,” Journal of Machine Learning Research , vol. 16, no. 1, pp. 1437–1480, 2015
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J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International conference on machine learning . PMLR, 2015, pp. 1889–1897
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2015
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M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch et al. , “Anymal-a highly mobile and dynamic quadrupedal robot,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2016, pp. 38–44
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J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, “Control of a quadrotor with reinforcement learning,” IEEE Robotics and Automation Letters , vol. 2, no. 4, pp. 2096–2103, 2017
2017
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Y. Chow, M. Ghavamzadeh, L. Janson, and M. Pavone, “Risk-constrained reinforcement learning with percentile risk criteria,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 6070–6120, 2017
2017
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J. Achiam, D. Held, A. Tamar, and P. Abbeel, “Constrained policy optimization,” in International conference on machine learning . PMLR, 2017, pp. 22–31
2017
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2017
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X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,” ACM Transactions On Graphics (TOG) , vol. 37, no. 4, pp. 1–14, 2018
2018
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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
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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) . IEEE, 2018, pp. 1–9
2018
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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
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2018
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W. Yu, G. Turk, and C. K. Liu, “Learning symmetric and low-energy locomotion,” ACM Transactions on Graphics (TOG) , vol. 37, no. 4, pp. 1–12, 2018
2018
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J. Hwangbo, J. Lee, and M. Hutter, “Per-contact iteration method for solving contact dynamics,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 895–902, 2018
2018
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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, p. eaau5872, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
B. Katz, J. Di Carlo, and S. Kim, “Mini cheetah: A platform for pushing the limits of dynamic quadruped control,” in 2019 international conference on robotics and automation (ICRA) . IEEE, 2019, pp. 6295–6301
2019
Cited alongside, same era.
Z. Fu, X. Cheng, and D. Pathak, “Deep whole-body control: Learning a unified policy for manipulation and locomotion,” in Conference on Robot Learning (CoRL) , 2022
2022
Later among the works it cites.
F. Jenelten, R. Grandia, F. Farshidian, and M. Hutter, “Tamols: Terrain-aware motion optimization for legged systems,” IEEE Transactions on Robotics , vol. 38, no. 6, pp. 3395–3413, 2022
2022
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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 , vol. 7, no. 62, p. eabk2822, 2022
2022
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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
2022
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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
2020
Cited alongside, same era.
O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Cited alongside, same era.
S. Gangapurwala, A. Mitchell, and I. Havoutis, “Guided constrained policy optimization for dynamic quadrupedal robot locomotion,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3642–3649, 2020
2020
Cited alongside, same era.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning . PMLR, 2020, pp. 1101–1112
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Liu, J. Ding, and X. Liu, “Ipo: Interior-point policy optimization under constraints,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 04, 2020, pp. 4940–4947
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Xie, H. Y. Ling, N. H. Kim, and M. van de Panne, “Allsteps: curriculum-driven learning of stepping stone skills,” in Computer Graphics Forum , vol. 39, no. 8. Wiley Online Library, 2020, pp. 213–224
2020
Cited alongside, same era.
G. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” in Proceedings of Robotics: Science and Systems , 2022
2022
Later among the works it cites.
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 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 25–32
2022
Later among the works it cites.
T.-Y. Yang, T. Zhang, L. Luu, S. Ha, J. Tan, and W. Yu, “Safe reinforcement learning for legged locomotion,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 2454–2461
2022
Later among the works it cites.
2022
Later among the works it cites.
Y.-H. Shin, S. Hong, S. Woo, J. Choe, H. Son, G. Kim, J.-H. Kim, K. Lee, J. Hwangbo, and H.-W. Park, “Design of kaist hound, a quadruped robot platform for fast and efficient locomotion with mixed-integer nonlinear optimization of a gear train,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6614–6620
2022
Later among the works it cites.
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023
2023
Closest in time.
S. Choi, G. Ji, J. Park, H. Kim, J. Mun, J. H. Lee, and J. Hwangbo, “Learning quadrupedal locomotion on deformable terrain,” Science Robotics , vol. 8, no. 74, p. eade2256, 2023
2023
Closest in time.
2023
Closest in time.
R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive locomotion through nonlinear model-predictive control,” IEEE Transactions on Robotics , 2023
2023
Closest in time.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in Conference on Robot Learning . PMLR, 2023, pp. 403–415
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023
2023
Closest in time.
Y. Fuchioka, Z. Xie, and M. Van de Panne, “Opt-mimic: Imitation of optimized trajectories for dynamic quadruped behaviors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5092–5098
2023
Closest in time.
D. Kim, “Safe-RL-Algos,” Jun. 2023. [Online]. Available: https://github.com/dobro12/Safe-RL-Algos
2023
Closest in time.