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Found 33 papers, 12 papers with code
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates.
CART: Caltech Aerial RGB-Thermal Dataset in the Wild
We present the first publicly available RGB-thermal dataset designed for aerial robotics operating in natural environments.
Generalizability Under Sensor Failure: Tokenization + Transformers Enable More Robust Latent Spaces
A major goal in neuroscience is to discover neural data representations that generalize.
RGB-X Object Detection via Scene-Specific Fusion Modules
Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions.
Online Self-Supervised Thermal Water Segmentation for Aerial Vehicles
We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals.
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy.
Predictive Control of Linear Discrete-Time Markovian Jump Systems by Learning Recurrent Patterns
Incorporating pattern-learning for prediction (PLP) in many discrete-time or discrete-event systems allows for computation-efficient controller design by memorizing patterns to schedule control policies based on their future occurrences.
Interstellar Object Accessibility and Mission Design
Interstellar objects (ISOs) are fascinating and under-explored celestial objects, providing physical laboratories to understand the formation of our solar system and probe the composition and properties of material formed in exoplanetary systems.
Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
This work presents a new method for unsupervised thermal image classification and semantic segmentation by transferring knowledge from the RGB domain using a multi-domain attention network.
Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees.
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.
Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness
Therefore, the training data domain should include spacecraft trajectories to accurately evaluate the learned model's safety and robustness.
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov function, the existence of which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i. e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other.
A Two-Part Controller Synthesis Approach for Nonlinear Stochastic Systems Perturbed by Lévy Noise Using Renewal Theory and HJB-Based Impulse Control
We are motivated by the lack of discussion surrounding methodological control design procedures for nonlinear shot and L\'evy noise stochastic systems to propose a hierarchical controller synthesis method with two parts.
Meta-Adaptive Nonlinear Control: Theory and Algorithms
We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.
Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty
We also present the predictor-corrector extension (gPC-SCP$^\mathrm{PC}$) for real-time motion trajectory generation in the presence of stochastic uncertainty.
H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch
We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment to minimize expected customer waiting times.
Incremental Nonlinear Stability Analysis of Stochastic Systems Perturbed by Lévy Noise
The convergence rate for shot noise systems is the same as the exponentially-stable nominal system, but with a tradeoff between the parameters of the shot noise process and the size of the error ball.
Learning-based Adaptive Control using Contraction Theory
Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance.
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach
This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory.
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity.
The Power of Predictions in Online Control
We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics.
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems.
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).
Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases.
Systems and Control Robotics Systems and Control
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
We design a stable nonlinear tracking controller using the learned model.
GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning.
Robotics
Online Optimization with Memory and Competitive Control
This paper presents competitive algorithms for a novel class of online optimization problems with memory.
Robust Regression for Safe Exploration in Control
To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.
Neural Lander: Stable Drone Landing Control using Learned Dynamics
To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
