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Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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Some controls applications of sum of squares programming
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
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Bregman divergence as general framework to estimate unnormalized statistical models
Michael U Gutmann and Jun-ichiro Hirayama · 2011
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Control design along trajectories with sums of squares programming
Anirudha Majumdar, Amir Ali Ahmadi, and Russ Tedrake · 2013
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Control barrier function based quadratic programs with application to adaptive cruise control
A. D. Ames, J. W. Grizzle, and P. Tabuada · 2014
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Deepmpc: Learning deep latent features for model predictive control
Ian Lenz, Ross A Knepper, and Ashutosh Saxena · 2015
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Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Stable reinforcement learning with autoencoders for tactile and visual data
Herke Van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, and Jan Peters · 2016
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Control barrier function based quadratic programs for safety critical systems
A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada · 2017
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Safe visual navigation via deep learning and novelty detection
Charles Richter and Nicholas Roy · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation
Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, and Sergey Levine · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Neural lyapunov control
Ya-Chien Chang, Nima Roohi, and Sicun Gao · 2019
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Pointwise feasibility of gaussian process-based safety-critical control under model uncertainty
Fernando Castañeda, Jason J. Choi, Bike Zhang, Claire J. Tomlin, and Koushil Sreenath · 2021
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Robust control barrier–value functions for safety-critical control
Jason J Choi, Donggun Lee, Koushil Sreenath, Claire J Tomlin, and Sylvia L Herbert · 2021
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Guaranteeing safety of learned perception modules via measurement-robust control barrier functions
Sarah Dean, Andrew Taylor, Ryan Cosner, Benjamin Recht, and Aaron Ames · 2021
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Control barriers in bayesian learning of system dynamics
Vikas Dhiman, Mohammad Javad Khojasteh, Massimo Franceschetti, and Nikolay Atanasov · 2021
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Learning robust output control barrier functions from safe expert demonstrations
Lars Lindemann, Alexander Robey, Lejun Jiang, Stephen Tu, and Nikolai Matni · 2021
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Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Robustness to out-of-distribution inputs via task-aware generative uncertainty
Rowan McAllister, Gregory Kahn, Jeff Clune, and Sergey Levine · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Solar: Deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, and Sergey Levine · 2019
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Control barrier functions for unknown nonlinear systems using gaussian processes
Pushpak Jagtap, George J Pappas, and Majid Zamani · 2020
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Neural certificates for safe control policies
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Learning barrier functions with memory for robust safe navigation
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Robust safety-critical control for dynamic robotics
Q. Nguyen and K. Sreenath · 2021
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Towards robust data-driven control synthesis for nonlinear systems with actuation uncertainty
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Self-supervised online learning for safety-critical control using stereo vision
Ryan K Cosner, Ivan D Jimenez Rodriguez, Tamas G Molnar, Wyatt Ubellacker, Yisong Yue, Aaron D Ames, and Katherine L Bouman · 2022
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Convex synthesis and verification of control-lyapunov and barrier functions with input constraints
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Safe nonlinear control using robust neural lyapunov-barrier functions
Charles Dawson, Zengyi Qin, Sicun Gao, and Chuchu Fan · 2022
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Lyapunov density models: Constraining distribution shift in learning-based control
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Sablas: Learning safe control for black-box dynamical systems
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Safety verification and controller synthesis for systems with input constraints
Han Wang, Kostas Margellos, and Antonis Papachristodoulou · 2022
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Ls3: Latent space safe sets for long-horizon visuomotor control of sparse reward iterative tasks
Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan, Joseph E Gonzalez, and Ken Goldberg · 2022
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