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Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics.
Feedback Control of Dynamic Bipedal Robot Locomotion
E. R. Westervelt, J. W. Grizzle, C. Chevallereau, J. H. Choi, and B. Morris · 1903
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Stabilization with relaxed controls
Zvi Artstein · 1983
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Persistent excitation in adaptive systems
Kumpati S Narendra and Anuradha M Annaswamy · 1987
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A ’universal’ contruction of Artstein’s theorem on nonlinear stabilization
E. Sontag · 1989
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Control lyapunov functions for adaptive nonlinear stabilization
Miroslav Krstić and Peter V Kokotović · 1995
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On characterizations of the input-to-state stability property
Eduardo D Sontag and Yuan Wang · 1995
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Nonlinear Systems: Analysis, Stability and Control
S. S. Sastry · 1999
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Nonlinear Systems - 3rd Edition
H.K. Khalil · 2002
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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A distribution-free theory of nonparametric regression
László Györfi, Michael Kohler, Adam Krzyzak, and Harro Walk · 2006
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Input to state stability: Basic concepts and results
Eduardo D Sontag · 2008
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Search-based structured prediction
Hal Daumé, John Langford, and Daniel Marcu · 2009
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No-regret reductions for imitation learning and structured prediction
Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell · 2010
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Learning control in robotics
Stefan Schaal and Christopher G Atkeson · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Towards the unification of locomotion and manipulation through control lyapunov functions and quadratic programs
Aaron D Ames and Matthew Powell · 2013
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Sufficient conditions for the lipschitz continuity of qp-based multi-objective control of humanoid robots
Benjamin Morris, Matthew J Powell, and Aaron D Ames · 2013
Cited alongside, same era.
Rapidly exponentially stabilizing control lyapunov functions and hybrid zero dynamics
Aaron D Ames, Kevin Galloway, Koushil Sreenath, and Jessy W Grizzle · 2014
Cited alongside, same era.
Safe and robust learning control with gaussian processes
Felix Berkenkamp and Angela P Schoellig · 2015
Cited alongside, same era.
Torque saturation in bipedal robotic walking through control lyapunov function-based quadratic programs
Bipedal robotic running with durus-2d: Bridging the gap between theory and experiment
Wen-Loong Ma, Shishir Kolathaya, Eric R Ambrose, Christian M Hubicki, and Aaron D Ames · 2017
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Learning lyapunov (potential) functions from counterexamples and demonstrations
Hadi Ravanbakhsh and Sriram Sankaranarayanan · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Deeply aggrevated: Differentiable imitation learning for sequential prediction
Wen Sun, Arun Venkatraman, Geoffrey J Gordon, Byron Boots, and J Andrew Bagnell · 2017
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A lyapunov-based approach to safe reinforcement learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2018
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Kevin Galloway, Koushil Sreenath, Aaron D Ames, and Jessy W Grizzle · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Optimal robust control for bipedal robots through control lyapunov function based quadratic programs
Quan Nguyen and Koushil Sreenath · 2015
Cited alongside, same era.
Safe learning of regions of attraction for uncertain, nonlinear systems with gaussian processes
Felix Berkenkamp, Riccardo Moriconi, Angela P Schoellig, and Andreas Krause · 2016
Cited alongside, same era.
Safe controller optimization for quadrotors with gaussian processes
Felix Berkenkamp, Angela P Schoellig, and Andreas Krause · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 2016
Cited alongside, same era.
Smooth imitation learning for online sequence prediction
Hoang M Le, Andrew Kang, Yisong Yue, and Peter Carr · 2016
Cited alongside, same era.
A general safety framework for learning-based control in uncertain robotic systems
Jaime F Fisac, Anayo K Akametalu, Melanie N Zeilinger, Shahab Kaynama, Jeremy Gillula, and Claire J Tomlin · 2018
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Towards a framework for realizable safety critical control through active set invariance
Thomas Gurriet, Andrew Singletary, Jacob Reher, Laurent Ciarletta, Eric Feron, and Aaron Ames · 2018
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Adaptive control by regulation-triggered batch least-squares estimation of non-observable parameters
Iasson Karafyllis, Maria Kontorinaki, and Miroslav Krstic · 2018
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The lyapunov neural network: Adaptive stability certification for safe learning of dynamic systems
Spencer M Richards, Felix Berkenkamp, and Andreas Krause · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Safe learning of quadrotor dynamics using barrier certificates
Li Wang, Evangelos A Theodorou, and Magnus Egerstedt · 2018
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Stable gaussian process based tracking control of euler–lagrange systems
Thomas Beckers, Dana Kulić, and Sandra Hirche · 2019
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Accelerating imitation learning with predictive models
Ching-An Cheng, Xinyan Yan, Evangelos Theodorou, and Byron Boots · 2019
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Neural lander: Stable drone landing control using learned dynamics
Guanya Shi, Xichen Shi, Michael O’Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, and Soon-Jo Chung · 2019
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