198 citations · 235 across the 13 of their papers we have counts for
13 papers
KALIKO: Kalman-Implicit Koopman Operator Learning For Prediction of Nonlinear Dynamical Systems
Albert H. Li, Ivan Dario Jimenez Rodriguez, Joel W. Burdick +2
Long-horizon dynamical prediction is fundamental in robotics and control, underpinning canonical methods like model predictive control. Yet, many systems and disturbance phenomena…
Robust Agility via Learned Zero Dynamics Policies
Noel Csomay-Shanklin, William D. Compton, Ivan Dario Jimenez Rodriguez +3
We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a map…
Constructive Nonlinear Control of Underactuated Systems via Zero Dynamics Policies
William Compton, Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin +2
Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the…
FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs
Yujia Huang, Ivan Dario Jimenez Rodriguez, Huan Zhang +2
Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also…
Safe Drone Flight with Time-Varying Backup Controllers
Andrew Singletary, Aiden Swann, Ivan Dario Jimenez Rodriguez +1
The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, hi…
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies
Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue +1
This work presents Neural Gaits, a method for learning dynamic walking gaits through the enforcement of set invariance that can be refined episodically using experimental data from…
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Ryan K. Cosner, Ivan D. Jimenez Rodriguez, Tamas G. Molnar +4
With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement err…
LyaNet: A Lyapunov Framework for Training Neural ODEs
Ivan Dario Jimenez Rodriguez, Aaron D. Ames, Yisong Yue
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 Lya…
Learning First-Order Representations for Planning from Black-Box States: New Results
Ivan D. Rodriguez, Blai Bonet, Javier Romero +1
Recently Bonet and Geffner have shown that first-order representations for planning domains can be learned from the structure of the state space without any prior knowledge about t…
Flexible FOND Planning with Explicit Fairness Assumptions
Ivan D. Rodriguez, Blai Bonet, Sebastian Sardina +1
We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are give…
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames +1
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model. Specifically, we show how to exploit the differentiability of…
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez +2
Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot…
Differentiable MPC for End-to-end Planning and Control
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks +2
We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one…