10 citations · 24 across the 6 of their papers we have counts for
6 papers
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…
Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems
Giorgos Mamakoukas, Maria L. Castano, Xiaobo Tan +1
This paper presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitud…
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control
Giorgos Mamakoukas, Orest Xherija, T. D. Murphey
Learning a stable Linear Dynamical System (LDS) from data involves creating models that both minimize reconstruction error and enforce stability of the learned representation. We p…
Learning Stable Models for Prediction and Control
Giorgos Mamakoukas, Ian Abraham, Todd D. Murphey
This paper demonstrates the benefits of imposing stability on data-driven Koopman operators. The data-driven identification of stable Koopman operators (DISKO) is implemented using…
Feedback Synthesis For Underactuated Systems Using Sequential Second-Order Needle Variations
Giorgos Mamakoukas, Malcolm A. MacIver, Todd D. Murphey
This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions---the second-order needle variations of optimal control---as t…
Feedback Synthesis for Controllable Underactuated Systems using Sequential Second Order Actions
Giorgos Mamakoukas, Malcolm A. MacIver, Todd D. Murphey
This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions---the needle variations of optimal control---as the basis for…