Search Results for author:
Found 56 papers, 12 papers with code
Towards a Robust Retrieval-Based Summarization System
SummRAG is an example of our goal of defining structured methods to test the capabilities of an LLM, rather than addressing issues in a one-off fashion.
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.
Residual-based Language Models are Free Boosters for Biomedical Imaging
In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data.
Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input.
Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds
Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity.
ReConTab: Regularized Contrastive Representation Learning for Tabular Data
In response to this challenge, we introduce ReConTab, a deep automatic representation learning framework with regularized contrastive learning.
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.
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing
We demonstrate the effectiveness of our method in improving few-shot learning performance on two key remote sensing datasets: Agriculture-Vision and EuroSAT.
Hallucination Improves the Performance of Unsupervised Visual Representation Learning
Contrastive learning models based on Siamese structure have demonstrated remarkable performance in self-supervised learning.
Coordinated Path Following of UAVs over Time-Varying Digraphs Connected in an Integral Sense
This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following.
Coordinated Path Following of UAVs using Event-Triggered Communication over Time-Varying Networks with Digraph Topologies
In this article, a novel time-coordination algorithm based on event-triggered communications is proposed to achieve coordinated path-following of UAVs.
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions.
An Information-Theoretic Analysis of Discrete-Time Control and Filtering Limitations by the I-MMSE Relationships
Fundamental limitations or performance trade-offs/limits are important properties and constraints of control and filtering systems.
Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
First, we generate and release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) to include raw, full-field imagery for greater experimental flexibility.
Balanced Training for Sparse GANs
We also introduce a new method called balanced dynamic sparse training (ADAPT), which seeks to control the BR during GAN training to achieve a good trade-off between performance and computational cost.
$\mathcal{L}_1$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.
Certified Robust Control under Adversarial Perturbations
Autonomous systems increasingly rely on machine learning techniques to transform high-dimensional raw inputs into predictions that are then used for decision-making and control.
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.
Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles
The synergistic safety layer uses only verifiable and logically analyzable software to fulfill its tasks.
Verifiable Obstacle Detection
Perception of obstacles remains a critical safety concern for autonomous vehicles.
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.
Path Integral Methods with Stochastic Control Barrier Functions
In this paper, we utilize Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the sample-based algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation.
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.
Simplified Analysis on Filtering Sensitivity Trade-offs in Continuous- and Discrete-Time Systems
A simplified analysis is performed on the Bode-type filtering sensitivity trade-off integrals, which capture the sensitivity characteristics of the estimate and estimation error with respect to the process input and estimated signal in continuous- and discrete-time linear time-invariant filtering systems.
Protective Mission against a Highly Maneuverable Rogue Drone Using Defense Margin Strategy
We introduce a new concept of defense margin to complement an existing strategy and construct a control strategy that successfully solves our problem.
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.
Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency
For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods.
Time Coordination of Multiple UAVs over Switching Communication Networks with Digraph Topologies
This paper presents a time-coordination algorithm for multiple UAVs executing cooperative missions.
Constrained Attack-Resilient Estimation of Stochastic Cyber-Physical Systems
In this paper, a constrained attack-resilient estimation algorithm (CARE) is developed for stochastic cyber-physical systems.
Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems
The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task.
$\mathcal{L}_1$ 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.
Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery
Advances in remote sensing technology have led to the capture of massive amounts of data.
Backup Plan Constrained Model Predictive Control
To incorporate this new safety concept in control problems, we formulate a feasibility maximization problem that adopts additional (virtual) input horizons toward the alternative missions on top of the input horizon toward the primary mission.
Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems
Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this paper.
Residue Density Segmentation for Monitoring and Optimizing Tillage Practices
"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture.
Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery
Next, we construct our proposed spatiotemporal architecture, which combines a UNet with a convolutional LSTM layer, to accurately detect regions of the field showing NDS; this approach has an impressive IOU score of 0. 53.
Cooperative Path Integral Control for Stochastic Multi-Agent Systems
Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems.
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems
The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete- and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs.
f-Divergence Variational Inference
This paper introduces the $f$-divergence variational inference ($f$-VI) that generalizes variational inference to all $f$-divergences.
Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes
We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.
SL1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments
Due to the high dependence of vehicle dynamics on the driving environments, the proposed Simplex leverages the finite-time model learning to timely learn and update the vehicle model for $\mathcal{L}_{1}$ adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments.
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.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction
The safety of these agents is dependent on their ability to predict collisions with other vehicles' future trajectories for replanning and collision avoidance.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Multi-Spectral Visual Odometry without Explicit Stereo Matching
Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression.
Proximity Queries for Absolutely Continuous Parametric Curves
In motion planning problems for autonomous robots, such as self-driving cars, the robot must ensure that its planned path is not in close proximity to obstacles in the environment.
Robotics Computational Geometry Graphics
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets.
