Safe Control

1 Safe Control

This section covers approaches to ensuring safety in control systems, including robust control, risk-averse control, value-constrained control, state-constrained control and stability, and uncertain dynamical systems.

1.1 Robust Control

• Minimax analysis of stochastic problems, Shapiro A., Kleywegt A. (2002).
• Robust DP Robust Dynamic Programming, Iyengar G. (2005).
• Robust Planning and Optimization, Laumanns M. (2011). (lecture notes)
• Robust Markov Decision Processes, Wiesemann W., Kuhn D., Rustem B. (2012).
• Safe and Robust Learning Control with Gaussian Processes, Berkenkamp F., Schoellig A. (2015). 🎞️
• Tube-MPPI Robust Sampling Based Model Predictive Control with Sparse Objective Information, Williams G. et al. (2018). 🎞️
• Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning, Lukas Bronke et al. (2021). :octocat:

1.2 Risk-Averse Control

• A Comprehensive Survey on Safe Reinforcement Learning, García J., Fernández F. (2015).
• RA-QMDP Risk-averse Behavior Planning for Autonomous Driving under Uncertainty, Naghshvar M. et al. (2018).
• StoROO X-Armed Bandits: Optimizing Quantiles and Other Risks, Torossian L., Garivier A., Picheny V. (2019).
• Worst Cases Policy Gradients, Tang Y. C. et al. (2019).
• Model-Free Risk-Sensitive Reinforcement Learning, Delétang G. et al. (2021).
• Optimal Thompson Sampling strategies for support-aware CVaR bandits, Baudry D., Gautron R., Kaufmann E., Maillard O. (2021).

1.3 Value-Constrained Control

• ICS Will the Driver Seat Ever Be Empty?, Fraichard T. (2014).
• SafeOPT Safe Controller Optimization for Quadrotors with Gaussian Processes, Berkenkamp F., Schoellig A., Krause A. (2015). 🎞️ :octocat:
• SafeMDP Safe Exploration in Finite Markov Decision Processes with Gaussian Processes, Turchetta M., Berkenkamp F., Krause A. (2016). :octocat:
• RSS On a Formal Model of Safe and Scalable Self-driving Cars, Shalev-Shwartz S. et al. (2017).
• CPO Constrained Policy Optimization, Achiam J., Held D., Tamar A., Abbeel P. (2017). :octocat:
• RCPO Reward Constrained Policy Optimization, Tessler C., Mankowitz D., Mannor S. (2018).
• BFTQ A Fitted-Q Algorithm for Budgeted MDPs, Carrara N. et al. (2018).
• SafeMPC Learning-based Model Predictive Control for Safe Exploration, Koller T, Berkenkamp F., Turchetta M. Krause A. (2018).
• CCE Constrained Cross-Entropy Method for Safe Reinforcement Learning, Wen M., Topcu U. (2018). :octocat:
• LTL-RL Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving, Bouton M. et al. (2019).
• Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments, Bouton M. et al. (2019). :octocat:
• Batch Policy Learning under Constraints, Le H., Voloshin C., Yue Y. (2019).
• Value constrained model-free continuous control, Bohez S. et al (2019). 🎞️
• Safely Learning to Control the Constrained Linear Quadratic Regulator, Dean S. et al (2019).
• Learning to Walk in the Real World with Minimal Human Effort, Ha S. et al. (2020) 🎞️
• Responsive Safety in Reinforcement Learning by PID Lagrangian Methods, Stooke A., Achiam J., Abbeel P. (2020). :octocat:
• Envelope MOQ-Learning A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation, Yang R. et al (2019).

1.4 State-Constrained Control and Stability

• HJI-reachability Safe learning for control: Combining disturbance estimation, reachability analysis and reinforcement learning with systematic exploration, Heidenreich C. (2017).
• MPC-HJI On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions, Leung K. et al. (2018).
• A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems, Fisac J. et al (2017). 🎞️
• Safe Model-based Reinforcement Learning with Stability Guarantees, Berkenkamp F. et al. (2017).
• Lyapunov-Net Safe Interactive Model-Based Learning, Gallieri M. et al. (2019).
• Enforcing robust control guarantees within neural network policies, Donti P. et al. (2021). :octocat:
• ATACOM Robot Reinforcement Learning on the Constraint Manifold, Liu P. et al (2021).

1.5 Uncertain Dynamical Systems

• Simulation of Controlled Uncertain Nonlinear Systems, Tibken B., Hofer E. (1995).
• Trajectory computation of dynamic uncertain systems, Adrot O., Flaus J-M. (2002).
• Simulation of Uncertain Dynamic Systems Described By Interval Models: a Survey, Puig V. et al. (2005).
• Design of interval observers for uncertain dynamical systems, Efimov D., Raïssi T. (2016).