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 …
Learning for safety-critical control with control barrier functions
Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …
safety, and have been deployed successfully in multiple domains. Despite this success …
Lyanet: A lyapunov framework for training neural odes
We propose a method for training ordinary differential equations by using a control-theoretic
Lyapunov condition for stability. Our approach, called LyaNet, is based on a novel Lyapunov …
Lyapunov condition for stability. Our approach, called LyaNet, is based on a novel Lyapunov …
Bayesian learning-based adaptive control for safety critical systems
Deep learning has enjoyed much recent success, and applying state-of-the-art model
learning methods to controls is an exciting prospect. However, there is a strong reluctance to …
learning methods to controls is an exciting prospect. However, there is a strong reluctance to …
Neural operators for bypassing gain and control computations in pde backstepping
We introduce a framework for eliminating the computation of controller gain functions in PDE
control. We learn the nonlinear operator from the plant parameters to the control gains with a …
control. We learn the nonlinear operator from the plant parameters to the control gains with a …
Episodic learning with control lyapunov functions for uncertain robotic systems
Many modern nonlinear control methods aim to endow systems with guaranteed properties,
such as stability or safety, and have been successfully applied to the domain of robotics …
such as stability or safety, and have been successfully applied to the domain of robotics …
[HTML][HTML] Neural operators of backstepping controller and observer gain functions for reaction–diffusion PDEs
Unlike ODEs, whose models involve system matrices and whose controllers involve vector
or matrix gains, PDE models involve functions in those roles—functional coefficients …
or matrix gains, PDE models involve functions in those roles—functional coefficients …
Lyapunov design for robust and efficient robotic reinforcement learning
Recent advances in the reinforcement learning (RL) literature have enabled roboticists to
automatically train complex policies in simulated environments. However, due to the poor …
automatically train complex policies in simulated environments. However, due to the poor …
A control barrier perspective on episodic learning via projection-to-state safety
In this letter we seek to quantify the ability of learning to improve safety guarantees endowed
by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in …
by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in …
Neural Lyapunov control for nonlinear systems with unstructured uncertainties
S Wei, P Krishnamurthy… - 2023 American Control …, 2023 - ieeexplore.ieee.org
Stabilizing controller design and region of attraction (RoA) estimation are essential in
nonlinear control. Moreover, it is challenging to implement a control Lyapunov function …
nonlinear control. Moreover, it is challenging to implement a control Lyapunov function …