Search Results for author: Pan Zhao

Found 20 papers, 8 papers with code

The New Agronomists: Language Models are Experts in Crop Management

1 code implementation28 Mar 2024 Jing Wu, Zhixin Lai, Suiyao Chen, Ran Tao, Pan Zhao, Naira Hovakimyan

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.

Language Modelling Management +2

A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning

1 code implementation14 Oct 2023 Pan Zhao, Yifan Cui

In this article, we propose a general instrumented DiD approach for learning the optimal treatment policy.

Positivity-free Policy Learning with Observational Data

1 code implementation10 Oct 2023 Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang

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

no code implementations24 Sep 2023 Ran Tao, Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan

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.

Model Predictive Control

Learning Tube-Certified Control Using Robust Contraction Metrics

1 code implementation14 Sep 2023 Vivek Sharma, Pan Zhao, Naira Hovakimyan

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

no code implementations14 Feb 2023 Zhuohuan Wu, Sheng Cheng, Pan Zhao, Aditya Gahlawat, Kasey A. Ackerman, Arun Lakshmanan, Chengyu Yang, Jiahao Yu, Naira Hovakimyan

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

1 code implementation13 Jan 2023 Pan Zhao, Julie Josse, Shu Yang

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.

counterfactual Transfer Learning

Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions

no code implementations30 Nov 2022 Yikun Cheng, Pan Zhao, Naira Hovakimyan

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.

Computational Efficiency Efficient Exploration +4

Safety Embedded Stochastic Optimal Control of Networked Multi-Agent Systems via Barrier States

no code implementations8 Oct 2022 Lin Song, Pan Zhao, Neng Wan, Naira Hovakimyan

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

no code implementations20 Sep 2022 Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew T. Harrison, Carla Ferreira, Zahra Kalantari, Naira Hovakimyan

Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.

Imitation Learning Management +2

Integrated Adaptive Control and Reference Governors for Constrained Systems with State-Dependent Uncertainties

no code implementations5 Aug 2022 Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan

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

no code implementations21 Apr 2022 Jing Wu, Ran Tao, Pan Zhao, Nicolas F. Martin, Naira Hovakimyan

Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize.

Management reinforcement-learning +1

CVFNet: Real-time 3D Object Detection by Learning Cross View Features

no code implementations13 Mar 2022 Jiaqi Gu, Zhiyu Xiang, Pan Zhao, Tingming Bai, Lingxuan Wang, Xijun Zhao, Zhiyuan Zhang

In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies.

3D Object Detection Object +1

Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation

no code implementations15 Dec 2021 Pan Zhao, Ziyao Guo, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan

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

2 code implementations14 Sep 2021 Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman, Aditya Gahlawat, Arun Lakshmanan, Pan Zhao, Naira Hovakimyan

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

1 code implementation9 Sep 2021 Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan

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.

Motion Planning valid

$\mathcal{L}_1$ Adaptive Control with Switched Reference Models: Application to Learn-to-Fly

no code implementations19 Aug 2021 Steven Snyder, Pan Zhao, Naira Hovakimyan

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

no code implementations4 Jun 2021 Yikun Cheng, Pan Zhao, Manan Gandhi, Bo Li, Evangelos Theodorou, Naira Hovakimyan

A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations.

reinforcement-learning Reinforcement Learning (RL)

Robust Adaptive Control of Linear Parameter-Varying Systems with Unmatched Uncertainties

1 code implementation9 Oct 2020 Pan Zhao, Steven Snyder, Naira Hovakimyana, Chengyu Cao

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

no code implementations L4DC 2020 Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos Theodorou

We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning.

GPR regression

Cannot find the paper you are looking for? You can Submit a new open access paper.