68 citations · 76 across the 9 of their papers we have counts for
10 papers
Data-Driven Control with Inherent Lyapunov Stability
Youngjae Min, Spencer M. Richards, Navid Azizan
Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time. However,…
Learning Control-Oriented Dynamical Structure from Data
Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan +1
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…
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
Rohan Sinha, James Harrison, Spencer M. Richards +1
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…
A System-Level View on Out-of-Distribution Data in Robotics
Rohan Sinha, Apoorva Sharma, Somrita Banerjee +6
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…
Control-oriented meta-learning
Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine +1
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory…
Control Barrier Functions for Cyber-Physical Systems and Applications to NMPC
Jan Schilliger, Thomas Lew, Spencer M. Richards +3
Tractable safety-ensuring algorithms for cyber-physical systems are important in critical applications. Approaches based on Control Barrier Functions assume continuous enforcement,…
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
Rohan Sinha, James Harrison, Spencer M. Richards +1
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…
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine +1
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory…
Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
Sumeet Singh, Spencer M. Richards, Vikas Sindhwani +2
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key contribution is a control-theoretic regularizer…
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems
Spencer M. Richards, Felix Berkenkamp, Andreas Krause
Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance. However, such algorithms are…