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Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability.
Real-time obstacle avoidance for manipulators and mobile robots
Oussama Khatib · 1986
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Reactive sliding-mode algorithm for collision avoidance in robotic systems
Luis Gracia, Fabricio Garelli, and Antonio Sala · 2013
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Control barrier function based quadratic programs with application to adaptive cruise control
Aaron D Ames, Jessy W Grizzle, and Paulo Tabuada · 2014
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Control in a safe set: Addressing safety in human-robot interactions
Changliu Liu and Masayoshi Tomizuka · 2014
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Safe reinforcement learning via shielding
Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, and Ufuk Topcu · 2018
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Value constrained model-free continuous control
Steven Bohez, Abbas Abdolmaleki, Michael Neunert, Jonas Buchli, Nicolas Heess, and Raia Hadsell · 2019
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End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
Richard Cheng, Gábor Orosz, Richard M Murray, and Joel W Burdick · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Safe control algorithms using energy functions: A unified framework, benchmark, and new directions
Tianhao Wei and Changliu Liu · 2019
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Safe and sample-efficient reinforcement learning for clustered dynamic environments
Hongyi Chen and Changliu Liu · 2021
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Reachability-based trajectory safeguard (rts): A safe and fast reinforcement learning safety layer for continuous control
Yifei Simon Shao, Chao Chen, Shreyas Kousik, and Ram Vasudevan · 2021
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A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
Kim Peter Wabersich and Melanie N Zeilinger · 2021
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Model-free safe control for zero-violation reinforcement learning
Weiye Zhao, Tairan He, and Changliu Liu · 2021
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Provably safe reinforcement learning: Conceptual analysis, survey, and benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, and Matthias Althoff · 2022
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Projection-based constrained policy optimization
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge · 2020
Cited alongside, same era.
Learning to provably satisfy high relative degree constraints for black-box systems, 2024a
Jean-Baptiste Bouvier, Kartik Nagpal, and Negar Mehr
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Jean-Baptiste Bouvier, Kartik Nagpal, and Negar Mehr
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Guard: A safe reinforcement learning benchmark
Weiye Zhao, Rui Chen, Yifan Sun, Ruixuan Liu, Tianhao Wei, and Changliu Liu
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State-wise constrained policy optimization
Weiye Zhao, Rui Chen, Yifan Sun, Tianhao Wei, and Changliu Liu
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State-wise safe reinforcement learning: A survey
Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei, and Changliu Liu
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Implicit safe set algorithm for provably safe reinforcement learning, 2024a
Weiye Zhao, Tairan He, Feihan Li, and Changliu Liu
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Persistently feasible robust safe control by safety index synthesis and convex semi-infinite programming
Tianhao Wei, Shucheng Kang, Weiye Zhao, and Changliu Liu · 2022
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