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Found 63 papers, 9 papers with code
Risk-averse Learning with Non-Stationary Distributions
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time.
Time-Robust Path Planning with Piece-Wise Linear Trajectory for Signal Temporal Logic Specifications
We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Signal Temporal Logic (STL).
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time.
Infinite-horizon optimal scheduling for feedback control
Moreover, by the diagonal system matrix assumption, the optimal scheduling policy is shown to be of threshold type.
Whom to Trust? Elective Learning for Distributed Gaussian Process Regression
This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs).
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies.
Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system.
Innovation-triggered Learning for Data-driven Predictive Control: Deterministic and Stochastic Formulations
Two kinds of data selection mechanisms are proposed by online evaluating the innovation contained in the sampled data, wherein the innovation is quantified by its effect of shrinking the set of potential system dynamics that are compatible with the sampled data.
H2 suboptimal containment control of homogeneous and heterogeneous multi-agent systems
The aim is to design a distributed protocol by dynamic output feedback that achieves state/output containment control while the associated H2 cost is smaller than an a priori given upper bound.
Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Our approach requires no prior model and corresponds, to the best of our knowledge, to the first algorithm that guarantees safety in settings with occasionally infeasible safety certificates without requiring a backup non-learning-based controller.
Online Constraint Tightening in Stochastic Model Predictive Control: A Regression Approach
Solving chance-constrained stochastic optimal control problems is a significant challenge in control.
Risk-Sensitive Inhibitory Control for Safe Reinforcement Learning
We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success.
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
We address this issue by deriving a Bayesian prediction error bound for GP regression, which we show to decay with the growth of a novel, kernel-based measure of data density.
Koopman Kernel Regression
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e. g., the state of an agent or the reward of a policy.
Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning
When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data.
Time Series Classification for Detecting Parkinson's Disease from Wrist Motions
We used a random search to find the highest-scoring InceptionTime architecture and compared it to ROCKET with a ridge classifier and a multi-layer perceptron (MLP) on wrist motions of PD patients.
Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR).
Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing
We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process.
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version
In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound.
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based modeling.
Average Communication Rate for Event-Triggered Stochastic Control Systems
Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes.
Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions
Ensuring safety is of paramount importance in physical human-robot interaction applications.
Safe Reinforcement Learning via Confidence-Based Filters
Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems.
Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes
This paper proposes a physically consistent Gaussian Process (GP) enabling the identification of uncertain Lagrangian systems.
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model online from the data stream generated during operation.
Towards Data-driven LQR with Koopmanizing Flows
To utilize it for efficient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates.
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
When using Gaussian process regression to learn unknown systems, a commonly considered approach consists of learning the residual dynamics after applying some generic discretization technique, which might however disregard properties of the underlying physical system.
Diffeomorphically Learning Stable Koopman Operators
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling.
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes
To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency components in the incoming signal, regardless if they come from noise or the actual signal.
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach
We present a novel data-driven approach for learning linear representations of a class of stable nonlinear systems using Koopman eigenfunctions.
Personalized Rehabilitation Robotics based on Online Learning Control
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works.
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Our approach computes a confidence region in the space of hyperparameters, which enables us to obtain a probabilistic upper bound for the model error of a Gaussian process with arbitrary hyperparameters.
Distributed Value of Information in Feedback Control over Multi-hop Networks
In this paper we propose a distributed value of information (dVoI) metric for the joint design of control and schedulers for medium access in a multi-loop system and multi-hop network.
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy.
Distributed Bayesian Online Learning for Cooperative Manipulation
To overcome this shortcoming, we propose a novel distributed learning framework for the exemplary task of cooperative manipulation using Bayesian principles.
Inverse Reinforcement Learning: A Control Lyapunov Approach
A common approach to solve this problem is the framework of inverse reinforcement learning (IRL), where the observed agent, e. g., a human demonstrator, is assumed to behave according to an intrinsic cost function that reflects its intent and informs its control actions.
Value of information in networked control systems subject to delay
In this paper, we study the trade-off between the transmission cost and the control performance of the multi-loop networked control system subject to network-induced delay.
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms.
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed.
Koopman Operator Dynamical Models: Learning, Analysis and Control
The Koopman operator allows for handling nonlinear systems through a (globally) linear representation.
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency.
The Impact of Data on the Stability of Learning-Based Control- Extended Version
In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance.
Deep Learning based Uncertainty Decomposition for Real-time Control
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration.
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
To overcome this issue, we present a tracking control law for underactuated rigid-body dynamics using an online learning-based oracle for the prediction of the unknown dynamics.
Anticipating the Long-Term Effect of Online Learning in Control
Control schemes that learn using measurement data collected online are increasingly promising for the control of complex and uncertain systems.
Prediction with Approximated Gaussian Process Dynamical Models
The modeling and simulation of dynamical systems is a necessary step for many control approaches.
Real-Time Regression with Dividing Local Gaussian Processes
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models.
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems.
GP3: A Sampling-based Analysis Framework for Gaussian Processes
In order to overcome this issue, we propose a novel framework called GP3, general purpose computation on graphics processing units for Gaussian processes, which allows to solve many of the existing problems efficiently.
Parameter Optimization for Learning-based Control of Control-Affine Systems
By relaxing the problem through scenario optimization we derive a provably optimal method for control parameter tuning.
Smart Forgetting for Safe Online Learning with Gaussian Processes
The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles.
How Training Data Impacts Performance in Learning-based Control
When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations.
Localized active learning of Gaussian process state space models
We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy.
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model.
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control design.
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Finally, we derive safety conditions for the control of unknown dynamical systems based on Gaussian process models and evaluate them in simulations of a robotic manipulator.
Mean Square Prediction Error of Misspecified Gaussian Process Models
A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios.
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease
The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation.
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization
Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements.
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence.
Inverse Optimal Control with Incomplete Observations
We handle the problem by proposing the recovery matrix, which establishes a relationship between available observations of the trajectory and weights of given candidate features.
Robotics Systems and Control
Learning Stable Stochastic Nonlinear Dynamical Systems
A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e. g., from first principles in physics.
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training.
