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Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion.
N. Kohl and P. Stone, “Policy gradient reinforcement learning for fast quadrupedal locomotion,” IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04. 2004 , vol. 3, pp. 2619–2624 Vol.3, 2004
2004
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R. Tedrake, T. Zhang, and H. Seung, “Stochastic policy gradient reinforcement learning on a simple 3d biped,” 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) , vol. 3, pp. 2849–2854 vol.3, 2004
2004
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G. Endo, J. Morimoto, T. Matsubara, J. Nakanishi, and G. Cheng, “Learning cpg sensory feedback with policy gradient for biped locomotion for a full-body humanoid,” in AAAI , 2005
2005
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M. Kalakrishnan, J. Buchli, P. Pastor, M. N. Mistry, and S. Schaal, “Fast, robust quadruped locomotion over challenging terrain,” 2010 IEEE International Conference on Robotics and Automation , pp. 2665–2670, 2010
2010
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H. Dai, A. Valenzuela, and R. Tedrake, “Whole-body motion planning with centroidal dynamics and full kinematics,” in 2014 IEEE-RAS International Conference on Humanoid Robots . IEEE, 2014, pp. 295–302
2014
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N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, pp. 1929–1958, 2014
2014
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S. Kuindersma, R. Deits, M. Fallon, A. Valenzuela, H. Dai, F. Permenter, T. Koolen, P. Marion, and R. Tedrake, “Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot,” Autonomous robots , vol. 40, pp. 429–455, 2016
2016
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M. Hutter, C. Gehring, D. Jud, A. Lauber, D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Höpflinger, “Anymal - a highly mobile and dynamic quadrupedal robot,” IEEE International Conference on Intelligent Robots and Systems (IROS) , pp. 38–44, 2016
2016
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J. Tan, Z. Xie, B. Boots, and C. Liu, “Simulation-based design of dynamic controllers for humanoid balancing,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 2729–2736, 2016
2016
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J. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” ArXiv , vol. abs/1607.06450, 2016
2016
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C. Finn and S. Levine, “Deep visual foresight for planning robot motion,” 2017 IEEE International Conference on Robotics and Automation (ICRA) , pp. 2786–2793, 2017
2017
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H.-W. Park, P. M. Wensing, and S. Kim, “High-speed bounding with the mit cheetah 2: Control design and experiments,” The International Journal of Robotics Research , vol. 36, no. 2, pp. 167–192, 2017. [Online]. Available: https://doi.org/10.1177/0278364917694244
2017
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2017
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2018
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S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International Journal of Robotics Research , vol. 37, pp. 421 – 436, 2018
2018
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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, pp. 2261–2268, 2018
2018
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G. Bledt, M. J. Powell, B. Katz, J. Carlo, P. Wensing, and S. Kim, “Mit cheetah 3: Design and control of a robust, dynamic quadruped robot,” IEEE International Conference on Intelligent Robots and Systems (IROS) , pp. 2245–2252, 2018
2018
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2018
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2018
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2018
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J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018. [Online]. Available: http://github.com/google/jax
2018
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P. Shyam, W. Jaśkowski, and F. J. Gomez, “Model-based active exploration,” in International Conference on Machine Learning , 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:53102049
2018
Cited alongside, same era.
M. Zhang, S. Vikram, L. Smith, P. Abbeel, M. J. Johnson, and S. Levine, “Solar: Deep structured representations for model-based reinforcement learning,” in International Conference on Machine Learning (ICML) , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
B. Katz, J. Carlo, and S. Kim, “Mini cheetah: A platform for pushing the limits of dynamic quadruped control,” 2019 International Conference on Robotics and Automation (ICRA) , pp. 6295–6301, 2019
2019
Cited alongside, same era.
2021
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2021
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A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” Robotics: Science and Systems (RSS) , 2021
2021
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N. Rudin, H. Kolvenbach, V. Tsounis, and M. Hutter, “Cat-like jumping and landing of legged robots in low gravity using deep reinforcement learning,” IEEE Transactions on Robotics , vol. 38, pp. 317–328, 2021
2021
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W. Yu, V. Kumar, G. Turk, and C. Liu, “Sim-to-real transfer for biped locomotion,” 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 3503–3510, 2019
2019
Cited alongside, same era.
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. V. D. Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in Conference on Robot Learning (CoRL) , 2019
2019
Cited alongside, same era.
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, 2019
2019
Cited alongside, same era.
S. Choi and J. Kim, “Trajectory-based probabilistic policy gradient for learning locomotion behaviors,” 2019 International Conference on Robotics and Automation (ICRA) , pp. 1–7, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Ding, A. Pandala, C. Li, Y.-H. Shin, and H. won Park, “Representation-free model predictive control for dynamic motions in quadrupeds,” IEEE Transactions on Robotics , vol. 37, pp. 1154–1171, 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:229331611
2020
Cited alongside, same era.
G. Margolis, T. Chen, K. Paigwar, X. Fu, D. Kim, S. Kim, and P. Agrawal, “Learning to jump from pixels,” in Conference on Robot Learning , 2021
2021
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Z. Fu, A. Kumar, J. Malik, and D. Pathak, “Minimizing energy consumption leads to the emergence of gaits in legged robots,” Conference on Robot Learning (CoRL) , 2021
2021
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2021
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2022
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E. Nikishin, M. Schwarzer, P. D’Oro, P.-L. Bacon, and A. C. Courville, “The primacy bias in deep reinforcement learning,” in International Conference on Machine Learning , 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:248811264
2022
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G. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” in Robotics: Science and Systems , 2022
2022
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C. Yu and A. Rosendo, “Multi-modal legged locomotion framework with automated residual reinforcement learning,” IEEE Robotics and Automation Letters , vol. 7, pp. 10 312–10 319, 2022
2022
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2022
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2022
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2022
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K. Zakka, Y. Tassa, and MuJoCo Menagerie Contributors, “MuJoCo Menagerie: A collection of high-quality simulation models for MuJoCo,” 2022. [Online]. Available: http://github.com/deepmind/mujoco_menagerie
2022
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T. Hiraoka, T. Imagawa, T. Hashimoto, T. Onishi, and Y. Tsuruoka, “Dropout q-functions for doubly efficient reinforcement learning,” International Conference on Learning Representations (ICLR) , 2022
2022
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L. Smith, J. C. Kew, T. Li, L. Luu, X. B. Peng, S. Ha, J. Tan, and S. Levine, “Learning and adapting agile locomotion skills by transferring experience,” Robotics: Science and Systems (RSS) , 2023
2023
Closest in time.
L. Smith, I. Kostrikov, and S. Levine, “Demonstrating a walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning,” Robotics: Science and Systems (RSS) Demo , 2023
2023
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P. D’Oro, M. Schwarzer, E. Nikishin, P.-L. Bacon, M. G. Bellemare, and A. C. Courville, “Sample-efficient reinforcement learning by breaking the replay ratio barrier,” in International Conference on Learning Representations , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259298604
2023
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