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Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics.
M. Kalakrishnan, L. Righetti, P. Pastor, and S. Schaal, “Learning force control policies for compliant manipulation,” in
2011
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J. Buchli, F. Stulp, E. Theodorou, and S. Schaal, “Learning variable impedance control,”
2011
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E. Gribovskaya, A. Kheddar, and A. Billard, “Motion learning and adaptive impedance for robot control during physical interaction with humans,” in
2011
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F. Stulp, J. Buchli, A. Ellmer, M. Mistry, E. A. Theodorou, and S. Schaal, “Model-free reinforcement learning of impedance control in stochastic environments,”
2012
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K. Kronander and A. Billard, “Learning compliant manipulation through kinesthetic and tactile human-robot interaction,”
2013
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L. Saab, O. E. Ramos, F. Keith, N. Mansard, P. Soueres, and J.-Y. Fourquet, “Dynamic whole-body motion generation under rigid contacts and other unilateral constraints,”
2013
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J. Silvério, L. Rozo, S. Calinon, and D. G. Caldwell, “Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems,” in
2015
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2015
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A. Herzog, N. Rotella, S. Mason, F. Grimminger, S. Schaal, and L. Righetti, “Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid,”
2016
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E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,”
2016
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K. Kronander and A. Billard, “Stability considerations for variable impedance control,”
2016
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X. B. Peng and M. van de Panne, “Learning locomotion skills using deeprl: Does the choice of action space matter?,” in
2017
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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
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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,”
2019
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2019
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2019
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P. Varin, L. Grossman, and S. Kuindersma, “A comparison of action spaces for learning manipulation tasks,” in
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S. M. Khansari-Zadeh and O. Khatib, “Learning potential functions from human demonstrations with encapsulated dynamic and compliant behaviors,”
2017
Cited alongside, same era.
J. Viereck, J. Kozolinsky, A. Herzog, and L. Righetti, “Learning a structured neural network policy for a hopping task,”
2018
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2019
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R. Grandia, F. Farshidian, A. Dosovitskiy, R. Ranftl, and M. Hutter, “Frequency-aware model predictive control,”
2019
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Early access
F. Grimminger, A. Meduri, M. Khadiv, J. Viereck, M. Wüthrich, M. Naveau, V. Berenz, S. Heim, F. Widmaier, T. Flayols, J. Fiene, A. Badri-Spröwitz, and L. Righetti, “An open torque-controlled modular robot architecture for legged locomotion research,” · 2020
Closest in time.