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Found 9 papers, 5 papers with code
Data-Driven Control with Inherent Lyapunov Stability
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time.
Learning Control-Oriented Dynamical Structure from Data
Even for known nonlinear dynamical systems, feedback controller synthesis is a difficult problem that often requires leveraging the particular structure of the dynamics to induce a stable closed-loop system.
A System-Level View on Out-of-Distribution Data in Robotics
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack.
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
Control-oriented meta-learning
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments.
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments.
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics.
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
We demonstrate our method by learning the safe region of attraction for a simulated inverted pendulum.
