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Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion.
The correspondence problem
C. L. Nehaniv and K. Dautenhahn · 2002
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Rademacher and gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. H. Cho, and Y. Bengio · 2015
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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The benefit of multitask representation learning
A. Maurer, M. Pontil, and B. Romera-Paredes · 2016
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Nervenet: Learning structured policy with graph neural networks
T. Wang, R. Liao, J. Ba, and S. Fidler · 2018
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Learning when to concentrate or divert attention: Self-adaptive attention temperature for neural machine translation
J. Lin, X. Sun, X. Ren, M. Li, and Q. Su · 2018
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Sharing knowledge in multi-task deep reinforcement learning
C. D’Eramo, D. Tateo, A. Bonarini, M. Restelli, J. Peters, et al · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2020
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One policy to control them all: Shared modular policies for agent-agnostic control
W. Huang, I. Mordatch, and D. Pathak · 2020
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Blind bipedal stair traversal via sim-to-real reinforcement learning
J. Siekmann, K. Green, J. Warila, A. Fern, and J. Hurst · 2021
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Rma: Rapid motor adaptation for legged robots
A. Kumar, Z. Fu, D. Pathak, and J. Malik · 2021
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A cpg-based agile and versatile locomotion framework using proximal symmetry loss
M. Kasaei, M. Abreu, N. Lau, A. Pereira, and L. P. Reis · 2021
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My body is a cage: the role of morphology in graph-based incompatible control
V. Kurin, M. Igl, T. Rocktäschel, J. Böhmer, and S. Whiteson · 2021
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Learning robust perceptive locomotion for quadrupedal robots in the wild
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter · 2022
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Learning to walk in minutes using massively parallel deep reinforcement learning
N. Rudin, D. Hoeller, P. Reist, and M. Hutter · 2022
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Rapid locomotion via reinforcement learning
G. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal · 2022
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A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
L. Smith, I. Kostrikov, and S. Levine · 2022
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Grow your limits: Continuous improvement with real-world rl for robotic locomotion
L. Smith, Y. Cao, and S. Levine · 2023
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Real-world humanoid locomotion with reinforcement learning
I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath · 2023
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F. Jenelten, J. He, F. Farshidian, and M. Hutter · 2023
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Evaluation of constrained reinforcement learning algorithms for legged locomotion
J. Lee, L. Schroth, V. Klemm, M. Bjelonic, A. Reske, and M. Hutter · 2023
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A. Kumar, Z. Li, J. Zeng, D. Pathak, K. Sreenath, and J. Malik · 2022
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Learning and deploying robust locomotion policies with minimal dynamics randomization
L. Campanaro, S. Gangapurwala, W. Merkt, and I. Havoutis · 2022
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Anymorph: Learning transferable polices by inferring agent morphology
B. Trabucco, M. Phielipp, and G. Berseth · 2022
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Metamorph: learning universal controllers with transformers
A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei · 2022
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Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion
G. Ji, J. Mun, H. Kim, and J. Hwangbo · 2022
Cited alongside, same era.
Walk these ways: Tuning robot control for generalization with multiplicity of behavior
G. B. Margolis and P. Agrawal · 2023
Cited alongside, same era.
Learning quadrupedal locomotion on deformable terrain
S. Choi, G. Ji, J. Park, H. Kim, J. Mun, J. H. Lee, and J. Hwangbo · 2023
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M. Shafiee, G. Bellegarda, and A. Ijspeert · 2023
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Multi-task reinforcement learning with mixture of orthogonal experts
A. Hendawy, J. Peters, and C. D’Eramo · 2023
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Learning modular robot control policies
J. Whitman, M. Travers, and H. Choset · 2023
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Genloco: Generalized locomotion controllers for quadrupedal robots
G. Feng, H. Zhang, Z. Li, X. B. Peng, B. Basireddy, L. Yue, Z. Song, L. Yang, Y. Liu, K. Sreenath, et al · 2023
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Rl-x: A deep reinforcement learning library (not only) for robocup
N. Bohlinger and K. Dorer · 2023
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Not only rewards but also constraints: Applications on legged robot locomotion
Y. Kim, H. Oh, J. Lee, J. Choi, G. Ji, M. Jung, D. Youm, and J. Hwangbo · 2024
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Agile but safe: Learning collision-free high-speed legged locomotion
T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi · 2024
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Long-horizon locomotion and manipulation on a quadrupedal robot with large language models
Y. Ouyang, J. Li, Y. Li, Z. Li, C. Yu, K. Sreenath, and Y. Wu · 2024
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Germ: A generalist robotic model with mixture-of-experts for quadruped robot
W. Song, H. Zhao, P. Ding, C. Cui, S. Lyu, Y. Fan, and D. Wang · 2024
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Mat: Morphological adaptive transformer for universal morphology policy learning
B. Li, H. Li, Y. Zhu, and D. Zhao · 2024
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Moral: Learning morphologically adaptive locomotion controller for quadrupedal robots on challenging terrains
Z. Luo, Y. Dong, X. Li, R. Huang, Z. Shu, E. Xiao, and P. Lu · 2024
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