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Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments.
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C. M. Kellett and A. R. Teel · 2003
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Rabbit: A testbed for advanced control theory
C. Chevallereau, G. Abba, F. Plestan, E. Westervelt, C. C. de Wit, J. Grizzle, et al · 2003
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Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning
R. Julian, B. Swanson, G. S. Sukhatme, S. Levine, C. Finn, and K. Hausman · 2004
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Examples when nonlinear model predictive control is nonrobust
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Model predictive control: for want of a local control lyapunov function, all is not lost
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On the stability of receding horizon control with a general terminal cost
A. Jadbabaie and J. Hauser · 2005
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Relaxing dynamic programming
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Finite-time bounds for fitted value iteration
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Towards the unification of locomotion and manipulation through control lyapunov functions and quadratic programs
A. D. Ames and M. Powell · 2013
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Rapidly exponentially stabilizing control lyapunov functions and hybrid zero dynamics
A. D. Ames, K. Galloway, K. Sreenath, and J. W. Grizzle · 2014
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Stabilization with discounted optimal control
V. Gaitsgory, L. Grüne, and N. Thatcher · 2015
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How to discount deep reinforcement learning: Towards new dynamic strategies
V. François-Lavet, R. Fonteneau, and D. Ernst · 2015
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The dependence of effective planning horizon on model accuracy
N. Jiang, A. Kulesza, S. Singh, and R. Lewis · 2015
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Learning quadrupedal locomotion over challenging terrain
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter · 2020
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Learning agile robotic locomotion skills by imitating animals
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine · 2020
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Reward tweaking: Maximizing the total reward while planning for short horizons
C. Tessler and S. Mannor · 2020
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Learning min-norm stabilizing control laws for systems with unknown dynamics
T. Westenbroek, F. Castañeda, A. Agrawal, S. S. Sastry, and K. Sreenath · 2020
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Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
J. Choi, F. Castañeda, C. Tomlin, and K. Sreenath · 2020
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R. Postoyan, L. Buşoniu, D. Nešić, and J. Daafouz · 2016
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Stabilization with discounted optimal control: the discrete time case
V. Gaitsgory, L. Grüne, C. M. Kellett, and S. R. Weller · 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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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
Cited alongside, same era.
Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
Cited alongside, same era.
Learning to walk via deep reinforcement learning
T. Haarnoja, S. Ha, A. Zhou, J. Tan, G. Tucker, and S. Levine · 2018
Cited alongside, same era.
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
S. Levine, A. Kumar, G. Tucker, and J. Fu · 2020
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Linear servo base unit with inverted pendulum, Apr 2021
Quanser · 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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Reinforcement learning for robust parameterized locomotion control of bipedal robots
Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath · 2021
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Model-based meta-reinforcement learning for flight with suspended payloads
S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra, and S. Levine · 2021
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Learning a contact-adaptive controller for robust, efficient legged locomotion
X. Da, Z. Xie, D. Hoeller, B. Boots, A. Anandkumar, Y. Zhu, B. Babich, and A. Garg · 2021
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Heuristic-guided reinforcement learning
C.-A. Cheng, A. Kolobov, and A. Swaminathan · 2021
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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 · 2021
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Combining model-based design and model-free policy optimization to learn safe, stabilizing controllers
T. Westenbroek, A. Agrawal, F. Castañeda, S. S. Sastry, and K. Sreenath · 2021
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Gaussian process-based min-norm stabilizing controller for control-affine systems with uncertain input effects and dynamics
F. Castañeda, J. J. Choi, B. Zhang, C. J. Tomlin, and K. Sreenath · 2021
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On the effectiveness of fine-tuning versus meta-reinforcement learning
Z. Mandi, P. Abbeel, and S. James · 2022
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