Search Results for author:
Found 31 papers, 4 papers with code
Safe Barrier-Constrained Control of Uncertain Systems via Event-triggered Learning
While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics.
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes
Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction.
Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
A common method to solve this problem is inverse reinforcement learning (IRL), where the observed agent, e. g., a human demonstrator, is assumed to behave according to the optimization of an intrinsic cost function that reflects its intent and informs its control actions.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
