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 …
Learning safe control for multi-robot systems: Methods, verification, and open challenges
In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
agent systems (MAS), focusing on learning-based methods with safety considerations. We …
Neural graph control barrier functions guided distributed collision-avoidance multi-agent control
We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …
large-scale environments with potentially moving obstacles, where a large number of agents …
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 …
Model-free safe reinforcement learning through neural barrier certificate
Safety is a critical concern when applying reinforcement learning (RL) to real-world control
tasks. However, existing safe RL works either only consider expected safety constraint …
tasks. However, existing safe RL works either only consider expected safety constraint …
In-distribution barrier functions: Self-supervised policy filters that avoid out-of-distribution states
F Castaneda, H Nishimura… - … for Dynamics and …, 2023 - proceedings.mlr.press
Learning-based control approaches have shown great promise in performing complex tasks
directly from high-dimensional perception data for real robotic systems. Nonetheless, the …
directly from high-dimensional perception data for real robotic systems. Nonetheless, the …
Gcbf+: A neural graph control barrier function framework for distributed safe multi-agent control
Distributed, scalable, and safe control of large-scale multi-agent systems (MAS) is a
challenging problem. In this paper, we design a distributed framework for safe multi-agent …
challenging problem. In this paper, we design a distributed framework for safe multi-agent …
Quadue-ccm: Interpretable distributional reinforcement learning using uncertain contraction metrics for precise quadrotor trajectory tracking
Accuracy and stability are common requirements for Quadrotor trajectory tracking systems.
Designing an accurate and stable tracking controller remains challenging, particularly in …
Designing an accurate and stable tracking controller remains challenging, particularly in …
Multi-step model predictive safety filters: Reducing chattering by increasing the prediction horizon
FP Bejarano, L Brunke… - 2023 62nd IEEE …, 2023 - ieeexplore.ieee.org
Learning-based controllers have demonstrated su-perior performance compared to classical
controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the …
controllers in various tasks. However, providing safety guarantees is not trivial. Safety, the …