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An attached arm can significantly increase the applicability of legged robots to several mobile manipulation tasks that are not possible for the wheeled or tracked counterparts.
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Proximal policy optimization algorithms
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Minimizing energy consumption leads to the emergence of gaits in legged robots
Z. Fu, A. Kumar, J. Malik, and D. Pathak · 2021
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A. Kumar, Z. Fu, D. Pathak, and J. Malik · 2021
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Reinforcement learning for robust parameterized locomotion control of bipedal robots
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W. Yu, C. K. Liu, and G. Turk · 2018
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Alma-articulated locomotion and manipulation for a torque-controllable robot
C. D. Bellicoso, K. Krämer, M. Stäuble, D. Sako, F. Jenelten, M. Bjelonic, and M. Hutter · 2019
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Solving rubik’s cube with a robot hand
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, et al · 2019
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Learning agile and dynamic motor skills for legged robots
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter · 2019
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Environment probing interaction policies
W. Zhou, L. Pinto, and A. Gupta · 2019
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Sim-to-real transfer for biped locomotion
W. Yu, V. C. V. Kumar, G. Turk, and C. K. Liu · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
I. Clavera, A. Nagabandi, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn · 2019
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Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath · 2021
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Learning kinematic feasibility for mobile manipulation through deep reinforcement learning
D. Honerkamp, T. Welschehold, and A. Valada · 2021
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Isaac gym: High performance GPU-Based physics simulation for robot learning
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and Gavriel State · 2021
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Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators
Y. Ma, F. Farshidian, T. Miki, J. Lee, and M. Hutter · 2022
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Bi-manual manipulation and attachment via sim-to-real reinforcement learning
S. Kataoka, S. K. S. Ghasemipour, D. Freeman, and I. Mordatch · 2022
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Rapid locomotion via reinforcement learning
G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal · 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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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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Legged robots that keep on learning: Fine-tuning locomotion policies in the real world
L. Smith, J. C. Kew, X. B. Peng, S. Ha, J. Tan, and S. Levine · 2022
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Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers
R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang · 2022
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Coupling vision and proprioception for navigation of legged robots
Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak · 2022
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Fully autonomous real-world reinforcement learning with applications to mobile manipulation
C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine · 2022
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Error-aware imitation learning from teleoperation data for mobile manipulation
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín · 2022
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Do as I can, not as I say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. J. Ruano, K. Jeffrey, S. Jesmonth, N. J. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, K.-H. Lee, S. Levine, Y. Lu, L. Luu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Rettinghouse, D. Reyes, P. Sermanet, N. Sievers, C. Tan, A. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, S. Xu, and M. Yan · 2022
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Legged locomotion in challenging terrains using egocentric vision
A. Agarwal, A. Kumar, J. Malik, and D. Pathak · 2022
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