5 citations · 11 across the 11 of their papers we have counts for
12 papers
Real-Time Disturbance-Aware Koopman Model Predictive Control for Quadrotors in Spatially Varying Winds
Stella Kombo, Masih Haseli, Alejandro A. Stefan-Zavala +3
Reliable quadrotor flight in disturbed environments requires predictive models that remain accurate as unmodeled aerodynamic effects change during deployment. We introduce a distur…
Control Forward-Backward Consistency: Quantifying the Accuracy of Koopman Control Family Models
Masih Haseli, Jorge Cortés, Joel W. Burdick
This paper extends the forward-backward consistency index, originally introduced in Koopman modeling of systems without input, to the setting of control systems, providing a closed…
On the Existence of Koopman Linear Embeddings for Controlled Nonlinear Systems
Xu Shang, Masih Haseli, Jorge Cortés +1
Koopman linear representations have become a popular tool for control design of nonlinear systems, yet it remains unclear when such representations are exact. In this paper, we est…
Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning
Stella Kombo, Masih Haseli, Skylar X. Wei +1
Autonomous systems often must predict the motions of nearby agents from partial and noisy data. This paper asks and answers the question: "can we learn, in real-time, a nonlinear p…
Two Roads to Koopman Operator Theory for Control: Infinite Input Sequences and Operator Families
Masih Haseli, Igor Mezić, Jorge Cortés
The Koopman operator, originally defined for dynamical systems without input, has inspired many applications in control. Yet, the theoretical foundations underpinning this progress…
Koopman Operators in Robot Learning
Lu Shi, Masih Haseli, Giorgos Mamakoukas +5
Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as an alternative modeling and learning-based control method across various robotics sub-domai…
Invariance Proximity: Closed-Form Error Bounds for Finite-Dimensional Koopman-Based Models
Masih Haseli, Jorge Cortés
A popular way to approximate the Koopman operator's action on a finite-dimensional subspace of functions is via orthogonal projections. The quality of the projected model directly…
Modeling Nonlinear Control Systems via Koopman Control Family: Universal Forms and Subspace Invariance Proximity
Masih Haseli, Jorge Cortés
This paper introduces the Koopman Control Family (KCF), a mathematical framework for modeling general (not necessarily control-affine) discrete-time nonlinear control systems with…
Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition
Masih Haseli, Jorge Cortés
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of f…
Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations
Masih Haseli, Jorge Cortés
This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods. Given a dictionary of functions, these methods approximate the project…
Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction
Masih Haseli, Jorge Cortés
We present a parallel data-driven strategy to identify finite-dimensional functional spaces invariant under the Koopman operator associated to an unknown dynamical system. We build…
Learning Koopman Eigenfunctions and Invariant Subspaces from Data: Symmetric Subspace Decomposition
Masih Haseli, Jorge Cortés
This paper develops data-driven methods to identify eigenfunctions of the Koopman operator associated to a dynamical system and subspaces that are invariant under the operator. We…