Fetching the paper…
Reading the bibliography…
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities.
1901
Earlier work this paper cites.
1904
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1909
Earlier work this paper cites.
1910
Earlier work this paper cites.
Zhou K, Doyle J, Glover K. 1996. Robust and Optimal Control
1996
Earlier work this paper cites.
Altman E. 1999. Constrained Markov decision processes
1999
Earlier work this paper cites.
Khalil H. 2002. Nonlinear Systems
2002
Earlier work this paper cites.
Mezić I. 2003. Controllability, integrability and ergodicity. In Multidisciplinary Research in Control: The Mohammed Dahleh Symposium 2002
2002
Earlier work this paper cites.
Brafman RI, Tennenholtz M. 2002. R-max-a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
Jarvis-Wloszek Z, Feeley R, Tan W, Sun K, Packard A. 2003. Some controls applications of sum of squares programming
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
Dullerud G, Paganini F. 2005. A Course in Robust Control Theory: A Convex Approach
2005
Earlier work this paper cites.
Mayne D, Seron M, Raković S. 2005. Robust model predictive control of constrained linear systems with bounded disturbances. Automatica
2005
Earlier work this paper cites.
Nilim A, El Ghaoui L. 2005. Robust control of Markov decision processes with uncertain transition matrices. Operations Research
2005
Earlier work this paper cites.
Morimoto J, Doya K. 2005. Robust reinforcement learning. Neural Computation
2005
Earlier work this paper cites.
Mitchell I, Bayen A, Tomlin C. 2005. A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games. IEEE Transactions on Automatic Control
2005
Earlier work this paper cites.
Bristow D, Tharayil M, Alleyne A. 2006. A survey of iterative learning control. IEEE Control Systems Magazine
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
Ahn HS, Chen Y, Moore KL. 2007. Iterative learning control: Brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
2007
Earlier work this paper cites.
2008
Earlier work this paper cites.
Schilders WH, Van der Vorst HA, Rommes J. 2008. Model order reduction: theory, research aspects and applications
2008
Earlier work this paper cites.
Hovakimyan N, Cao C. 2010. L1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
Åström K, Wittenmark B. 2011. Computer-Controlled Systems: Theory and Design, Third Edition
2011
Earlier work this paper cites.
Sastry S, Bodson M. 2011. Adaptive Control: Stability, Convergence and Robustness
2011
Earlier work this paper cites.
Nguyen-Tuong D, Peters J. 2011. Model learning for robot control: a survey. Cognitive processing
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
Moldovan TM, Abbeel P. 2012. Safe Exploration in Markov Decision Processes
2012
Earlier work this paper cites.
Gillula JH, Tomlin CJ. 2012. Guaranteed safe online learning via reachability: tracking a ground target using a quadrotor
2012
Earlier work this paper cites.
Mueller FL, Schoellig AP, D’Andrea R. 2012. Iterative learning of feed-forward corrections for high-performance tracking
2012
Earlier work this paper cites.
Kober J, Bagnell JA, Peters J. 2013. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research
2013
Earlier work this paper cites.
Aswani A, Gonzalez H, Sastry SS, Tomlin C. 2013. Provably safe and robust learning-based model predictive control. Automatica
2013
Earlier work this paper cites.
Cooper J, Che J, Cao C. 2014. The use of learning in fast adaptation algorithms. International Journal of Adaptive Control and Signal Processing
2014
Earlier work this paper cites.
Grande RC, Chowdhary G, How JP. 2014. Experimental validation of Bayesian nonparametric adaptive control using Gaussian processes. Journal of Aerospace Information Systems
2014
Earlier work this paper cites.
Tanaskovic M, Fagiano L, Smith R, Morari M. 2014. Adaptive receding horizon control for constrained MIMO systems. Automatica
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
García J, Fern, o Fernández. 2015. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research
2015
Earlier work this paper cites.
Ghavamzadeh M, Mannor S, Pineau J, Tamar A. 2015. Bayesian reinforcement learning: A survey. Foundations and Trends® in Machine Learning
2015
Earlier work this paper cites.
Chowdhary G, Kingravi HA, How JP, Vela PA. 2015. Bayesian nonparametric adaptive control using Gaussian processes. IEEE Transactions on Neural Networks and Learning Systems
2015
Earlier work this paper cites.
Berkenkamp F, Schoellig AP. 2015. Safe and robust learning control with Gaussian processes
2015
Earlier work this paper cites.
Sui Y, Gotovos A, Burdick J, Krause A. 2015. Safe exploration for optimization with Gaussian processes. In Proceedings of the 32nd International Conference on Machine Learning
2015
Earlier work this paper cites.
Schulman J, Levine S, Abbeel P, Jordan M, Moritz P. 2015. Trust region policy optimization. In Proceedings of the 32nd International Conference on Machine Learning
2015
Earlier work this paper cites.
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, et al. 2015. Human-level control through deep reinforcement learning. Nature
2015
Earlier work this paper cites.
Burgner-Kahrs J, Rucker DC, Choset H. 2015. Continuum robots for medical applications: A survey. IEEE Transactions on Robotics
2015
Cited alongside, same era.
Gonçalves GA, Guay M. 2016. Robust discrete-time set-based adaptive predictive control for nonlinear systems. Journal of Process Control
2016
Cited alongside, same era.
Ostafew CJ, Schoellig AP, Barfoot TD. 2016. Robust constrained learning-based NMPC enabling reliable mobile robot path tracking. The International Journal of Robotics Research
2016
Cited alongside, same era.
Turchetta M, Berkenkamp F, Krause A. 2016. Safe exploration in finite Markov decision processes with Gaussian processes. In Advances in Neural Information Processing Systems
2016
Cited alongside, same era.
Ames AD, Coogan S, Egerstedt M, Notomista G, Sreenath K, Tabuada P. 2019. Control Barrier Functions: Theory and Applications
2019
Later among the works it cites.
Taylor AJ, Dorobantu VD, Le HM, Yue Y, Ames AD. 2019a. Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems*
2019
Later among the works it cites.
Ohnishi M, Wang L, Notomista G, Egerstedt M. 2019. Barrier-certified adaptive reinforcement learning with applications to brushbot navigation. IEEE Transactions on Robotics
2019
Later among the works it cites.
Taylor AJ, Dorobantu VD, Krishnamoorthy M, Le HM, Yue Y, Ames AD. 2019b. A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
2019
Later among the works it cites.
Bajcsy A, Bansal S, Bronstein E, Tolani V, Tomlin CJ. 2019. An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Wieber PB, Tedrake R, Kuindersma S. 2016. Modeling and control of legged robots. In Springer Handbook of Robotics
2016
Cited alongside, same era.
Boutilier JJ, Brooks SC, Janmohamed A, Byers A, Buick JE, et al. 2017. Optimizing a drone network to deliver automated external defibrillators. Circulation
2017
Cited alongside, same era.
Polydoros AS, Nalpantidis L. 2017. Survey of model-based reinforcement learning: Applications on robotics. Journal of Intelligent & Robotic Systems
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Rawlings J, Mayne D, Diehl M. 2017. Model Predictive Control: Theory, Computation, and Design
2017
Cited alongside, same era.
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. 2017. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine
2017
Cited alongside, same era.
Achiam J, Held D, Tamar A, Abbeel P. 2017. Constrained policy optimization. In Proceedings of the 34th International Conference on Machine Learning
2017
Cited alongside, same era.
2019
Later among the works it cites.
Fisac JF, Lugovoy NF, Rubies-Royo V, Ghosh S, Tomlin CJ. 2019b. Bridging Hamilton-Jacobi safety analysis and reinforcement learning
2019
Later among the works it cites.
Andersson JAE, Gillis J, Horn G, Rawlings JB, Diehl M. 2019. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation
2019
Later among the works it cites.
McKinnon CD, Schoellig AP. 2019. Learning Probabilistic Models for Safe Predictive Control in Unknown Environments
2019
Later among the works it cites.
Hewing L, Wabersich KP, Menner M, Zeilinger MN. 2020. Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems
2020
Later among the works it cites.
Chatzilygeroudis K, Vassiliades V, Stulp F, Calinon S, Mouret JB. 2020. A survey on policy search algorithms for learning robot controllers in a handful of trials. IEEE Transactions on Robotics
2020
Later among the works it cites.
Ravichandar H, Polydoros AS, Chernova S, Billard A. 2020. Recent advances in robot learning from demonstration. Annual Review of Control, Robotics, and Autonomous Systems
2020
Later among the works it cites.
Gahlawat A, Zhao P, Patterson A, Hovakimyan N, Theodorou E. 2020. L1-GP: L1 adaptive control with Bayesian learning. In Proceedings of the 2nd Conference on Learning for Dynamics and Control
2020
Later among the works it cites.
Köhler J, Kötting P, Soloperto R, Allgöwer F, Müller MA. 2020. A robust adaptive model predictive control framework for nonlinear uncertain systems. International Journal of Robust and Nonlinear Control
2020
Later among the works it cites.
Hewing L, Kabzan J, Zeilinger MN. 2020. Cautious model predictive control using Gaussian process regression. IEEE Transactions on Control Systems Technology
2020
Later among the works it cites.
Fan D, Agha A, Theodorou E. 2020. Deep Learning Tubes for Tube MPC
2020
Later among the works it cites.
McKinnon CD, Schoellig AP. 2020. Context-aware Cost Shaping to Reduce the Impact of Model Error in Receding Horizon Control
2020
Later among the works it cites.
2020
Later among the works it cites.
Zhang J, Cheung B, Finn C, Levine S, Jayaraman D. 2020. Cautious adaptation for reinforcement learning in safety-critical settings. In Proceedings of the 37th International Conference on Machine Learning
2020
Later among the works it cites.
Thananjeyan B, Balakrishna A, Rosolia U, Li F, McAllister R, et al. 2020. Safety augmented value estimation from demonstrations (SAVED): Safe deep model-based rl for sparse cost robotic tasks. IEEE Robotics and Automation Letters
2020
Later among the works it cites.
Satija H, Amortila P, Pineau J. 2020. Constrained Markov decision processes via backward value functions. In Proceedings of the 37th International Conference on Machine Learning
2020
Later among the works it cites.
Turchetta M, Krause A, Trimpe S. 2020. Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
2020
Later among the works it cites.
Lütjens B, Everett M, How JP. 2020. Certified adversarial robustness for deep reinforcement learning. In Proceedings of the Conference on Robot Learning
2020
Later among the works it cites.
Loquercio A, Kaufmann E, Ranftl R, Dosovitskiy A, Koltun V, Scaramuzza D. 2020. Deep drone racing: From simulation to reality with domain randomization. IEEE Transactions on Robotics
2020
Later among the works it cites.
Mehta B, Diaz M, Golemo F, Pal CJ, Paull L. 2020. Active domain randomization. In Proceedings of the Conference on Robot Learning
2020
Later among the works it cites.
Zhou S, Helwa MK, Schoellig AP. 2020. Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking. The International Journal of Robotics Research
2020
Later among the works it cites.
Jin M, Lavaei J. 2020. Stability-certified reinforcement learning: A control-theoretic perspective. IEEE Access
2020
Later among the works it cites.
Zhou Z, Oguz OS, Leibold M, Buss M. 2020. A general framework to increase safety of learning algorithms for dynamical systems based on region of attraction estimation. IEEE Transactions on Robotics
2020
Later among the works it cites.
Choi J, Castañeda F, Tomlin C, Sreenath K. 2020. Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
2020
Later among the works it cites.
Taylor AJ, Ames AD. 2020. Adaptive Safety with Control Barrier Functions
2020
Later among the works it cites.
Khojasteh MJ, Dhiman V, Franceschetti M, Atanasov N. 2020. Probabilistic safety constraints for learned high relative degree system dynamics. In Proceedings of the 2nd Conference on Learning for Dynamics and Control
2020
Later among the works it cites.
Chandak Y, Jordan S, Theocharous G, White M, Thomas PS. 2020. Towards safe policy improvement for non-stationary MDPs. In Advances in Neural Information Processing Systems
2020
Later among the works it cites.
Burnett K, Qian J, Du X, Liu L, Yoon DJ, et al. 2021. Zeus: A system description of the two-time winner of the collegiate SAE autodrive competition. Journal of Field Robotics
2021
Closest in time.
Osborne M, Shin HS, Tsourdos A. 2021. A Review of Safe Online Learning for Nonlinear Control Systems
2021
Closest in time.
2021
Closest in time.
Holicki T, Scherer CW, Trimpe S. 2021. Controller design via experimental exploration with robustness guarantees. IEEE Control Systems Letters
2021
Closest in time.
von Rohr A, Neumann-Brosig M, Trimpe S. 2021. Probabilistic robust linear quadratic regulators with Gaussian processes. In Proceedings of the 3rd Conference on Learning for Dynamics and Control
2021
Closest in time.
Greeff M, Schoellig AP. 2021. Exploiting differential flatness for robust learning-based tracking control using Gaussian processes. IEEE Control Systems Letters
2021
Closest in time.
Pereida K, Brunke L, Schoellig AP. 2021. Robust adaptive model predictive control for guaranteed fast and accurate stabilization in the presence of model errors. International Journal of Robust and Nonlinear Control
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Thananjeyan B, Balakrishna A, Nair S, Luo M, Srinivasan K, et al. 2021. Recovery RL: Safe reinforcement learning with learned recovery zones. IEEE Robotics and Automation Letters
2021
Closest in time.
2021
Closest in time.
Lopez BT, Slotine JJE, How JP. 2021. Robust adaptive control barrier functions: An adaptive and data-driven approach to safety. IEEE Control Systems Letters
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Wabersich KP, Zeilinger MN. 2021. A predictive safety filter for learning-based control of constrained nonlinear dynamical systems. Automatica
2021
Closest in time.
Liu CK, Negrut D. 2021. The role of physics-based simulators in robotics. Annual Review of Control, Robotics, and Autonomous Systems
2021
Closest in time.
Panerati J, Zheng H, Zhou S, Xu J, Prorok A, Schoellig AP. 2021. Learning to Fly—a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
2021
Closest in time.
Coumans E, Bai Y. 2016–2021. PyBullet, a Python module for physics simulation for games, robotics and machine learning. http://pybullet.org
2021
Closest in time.
Kiumarsi B, Vamvoudakis KG, Modares H, Lewis FL. 2018. Optimal and autonomous control using reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems
2062
Closest in time.