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Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that enables robots to simultaneously detect unsafe states and generate actions that prevent future failures.
On the theory of dynamic programming
Richard Bellman · 1952
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Discrete time high-order schemes for viscosity solutions of hamilton-jacobi-bellman equations
Marizio Falcone and Roberto Ferretti · 1994
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On reachability and minimum cost optimal control
John Lygeros · 2004
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A toolbox of level set methods
I. Mitchell · 2004
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A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games
Ian Mitchell, Alex Bayen, and Claire J. Tomlin · 2005
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Ltlmop: Experimenting with language, temporal logic and robot control
Cameron Finucane, Gangyuan Jing, and Hadas Kress-Gazit · 2010
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Hamilton–jacobi formulation for reach–avoid differential games
Kostas Margellos and John Lygeros · 2011
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Reach-avoid problems with time-varying dynamics, targets and constraints
J. Fisac, M. Chen, C. J. Tomlin, and S. Sastry · 2015
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Hamilton-jacobi reachability: A brief overview and recent advances
Somil Bansal, Mo Chen, Sylvia Herbert, and Claire J Tomlin · 2017
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Dynamic Programming and Optimal Control , volume 2, page 14–22
Dimitri Bertsekas · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Safe policy synthesis in multi-agent pomdps via discrete-time barrier functions
Mohamadreza Ahmadi, Andrew Singletary, Joel W Burdick, and Aaron D Ames · 2019
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Control barrier functions: Theory and applications
Aaron D Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada · 2019
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Bridging hamilton-jacobi safety analysis and reinforcement learning
Jaime F Fisac, Neil F Lugovoy, Vicenç Rubies-Royo, Shromona Ghosh, and Claire J Tomlin · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Robot motion planning in learned latent spaces
Brian Ichter and Marco Pavone · 2019
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Continuous control with deep reinforcement learning, 2019
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2019
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Maximum likelihood constraint inference for inverse reinforcement learning
Dexter RR Scobee and S Shankar Sastry · 2019
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Learning control barrier functions from expert demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V Dimarogonas, Stephen Tu, and Nikolai Matni · 2020
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Learning to be safe: Deep rl with a safety critic
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, and Chelsea Finn · 2020
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Analyzing human models that adapt online
Andrea Bajcsy, Anand Siththaranjan, Claire J Tomlin, and Anca D Dragan · 2021
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Deepreach: A deep learning approach to high-dimensional reachability
Somil Bansal and Claire J Tomlin · 2021
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Leaf: Latent exploration along the frontier
Homanga Bharadhwaj, Animesh Garg, and Florian Shkurti · 2021
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Mastering atari with discrete world models
Structured world models from human videos
Russell Mendonca, Shikhar Bahl, and Deepak Pathak · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Enforcing safety for vision-based controllers via control barrier functions and neural radiance fields
Mukun Tong, Charles Dawson, and Chuchu Fan · 2023
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Belief control barrier functions for risk-aware control
Matti Vahs, Christian Pek, and Jana Tumova · 2023
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Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Kim P Wabersich, Andrew J Taylor, Jason J Choi, Koushil Sreenath, Claire J Tomlin, Aaron D Ames, and Melanie N Zeilinger · 2023
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Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Safety and liveness guarantees through reach-avoid reinforcement learning
Kai-Chieh Hsu, Vicenç Rubies-Royo, Claire J Tomlin, and Jaime F Fisac · 2021
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Recovery rl: Safe reinforcement learning with learned recovery zones
Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, and Ken Goldberg · 2021
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Survey on mining signal temporal logic specifications
Ezio Bartocci, Cristinel Mateis, Eleonora Nesterini, and Dejan Nickovic · 2022
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Optimizeddp: An efficient, user-friendly library for optimal control and dynamic programming
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Temporal difference learning for model predictive control
Nicklas Hansen, Xiaolong Wang, and Hao Su · 2022
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Dynamics-aware metric embedding: Metric learning in a latent space for visual planning
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Sequential neural barriers for scalable dynamic obstacle avoidance. in 2023 ieee
H Yu, C Hirayama, C Yu, S Herbert, and S Gao · 2023
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Mastering diverse domains through world models, 2024
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Agile but safe: Learning collision-free high-speed legged locomotion
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Learning shared safety constraints from multi-task demonstrations
Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, and Steven Z Wu · 2024
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Albert Lin, Shuang Peng, and Somil Bansal · 2024
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Learning robust output control barrier functions from safe expert demonstrations
Lars Lindemann, Alexander Robey, Lejun Jiang, Satyajeet Das, Stephen Tu, and Nikolai Matni · 2024
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Learning safety constraints from demonstrations with unknown rewards
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, and Andreas Krause · 2024
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Model-based runtime monitoring with interactive imitation learning
Huihan Liu, Shivin Dass, Roberto Martín-Martín, and Yuke Zhu · 2024
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Morals: Analysis of high-dimensional robot controllers via topological tools in a latent space
Ewerton R Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, and Kostas E Bekris · 2024
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Dino-wm: World models on pre-trained visual features enable zero-shot planning
Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto · 2024
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Certifiable deep learning for reachability using a new lipschitz continuous value function, 2025
Jingqi Li, Donggun Lee, Jaewon Lee, Kris Shengjun Dong, Somayeh Sojoudi, and Claire Tomlin · 2025
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