Safe learning in robotics: From learning-based control to safe reinforcement learning
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 …
methods for real-world robotic deployments from both the control and reinforcement learning …
The safety filter: A unified view of safety-critical control in autonomous systems
Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …
by the expanding reach of robotic technologies. However, the emergence of new …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …
including the reliable integration of renewable energy sources into power grids, safe …
Control barrier functions and input-to-state safety with application to automated vehicles
Balancing safety and performance is one of the predominant challenges in modern control
system design. Moreover, it is crucial to robustly ensure safety without inducing unnecessary …
system design. Moreover, it is crucial to robustly ensure safety without inducing unnecessary …
Iterative reachability estimation for safe reinforcement learning
Ensuring safety is important for the practical deployment of reinforcement learning (RL).
Various challenges must be addressed, such as handling stochasticity in the environments …
Various challenges must be addressed, such as handling stochasticity in the environments …
How to train your neural control barrier function: Learning safety filters for complex input-constrained systems
Control barrier functions (CBFs) have become popular as a safety filter to guarantee the
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
safety of nonlinear dynamical systems for arbitrary inputs. However, it is difficult to construct …
Joint synthesis of safety certificate and safe control policy using constrained reinforcement learning
Safety is the major consideration in controlling complex dynamical systems using
reinforcement learning (RL), where the safety certificates can provide provable safety …
reinforcement learning (RL), where the safety certificates can provide provable safety …
Safety-critical control with input delay in dynamic environment
Endowing nonlinear systems with safe behavior is increasingly important in modern control.
This task is particularly challenging for real-life control systems that operate in dynamically …
This task is particularly challenging for real-life control systems that operate in dynamically …