Search Results for author:
Found 20 papers, 8 papers with code
The New Agronomists: Language Models are Experts in Crop Management
A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model's capacity to understand states and explore optimal management practices.
A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning
In this article, we propose a general instrumented DiD approach for learning the optimal treatment policy.
Positivity-free Policy Learning with Observational Data
Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity.
Robust Adaptive MPC Using Uncertainty Compensation
The performance bounds provided by the L1AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints.
Learning Tube-Certified Control Using Robust Contraction Metrics
In this paper, for nonlinear systems subject to bounded disturbances, we present a framework for jointly learning a robust nonlinear controller and a contraction metric using a novel disturbance rejection objective that certifies a universal $\mathcal L_\infty$ gain bound using NNs for user-specified variables.
$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees
Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications.
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data
We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population.
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions
Safety filters, e. g., based on control barrier functions (CBFs), provide a promising way for safe RL via modifying the unsafe actions of an RL agent on the fly.
Safety Embedded Stochastic Optimal Control of Networked Multi-Agent Systems via Barrier States
This paper presents a novel approach for achieving safe stochastic optimal control in networked multi-agent systems (MASs).
Optimizing Crop Management with Reinforcement Learning and Imitation Learning
Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.
Integrated Adaptive Control and Reference Governors for Constrained Systems with State-Dependent Uncertainties
The proposed framework leverages an L1 adaptive controller (L1AC) that estimates and compensates for the uncertainties, and provides guaranteed transient performance, in terms of uniform bounds on the error between actual states and inputs and those of a nominal (i. e., uncertainty-free) system.
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations
Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize.
CVFNet: Real-time 3D Object Detection by Learning Cross View Features
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies.
Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation
This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties.
$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors
This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.
Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics
This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.
$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly
Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control.
Robustifying Reinforcement Learning Policies with $\mathcal{L}_1$ Adaptive Control
A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations.
Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties
In controlling systems with large operating envelopes, it is often necessary to adjust the desired dynamics according to operating conditions.
L1-GP: L1 Adaptive Control with Bayesian Learning
We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning.
