research
∙
09/11/2023
Robust Nonlinear Reduced-Order Model Predictive Control
Real-world systems are often characterized by high-dimensional nonlinear...
share
research
∙
07/25/2023
Submodular Reinforcement Learning
In reinforcement learning (RL), rewards of states are typically consider...
research
∙
06/24/2023
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Physics-informed machine learning (PIML) is a set of methods and tools t...
research
∙
04/19/2023
Approximate non-linear model predictive control with safety-augmented neural networks
Model predictive control (MPC) achieves stability and constraint satisfa...
research
∙
03/16/2023
Error analysis of regularized trigonometric linear regression with unbounded sampling: a statistical learning viewpoint
The effectiveness of non-parametric, kernel-based methods for function e...
research
∙
03/13/2023
Multi-agent Distributed Model Predictive Control with Connectivity Constraint
In cooperative multi-agent robotic systems, coordination is necessary in...
research
∙
11/18/2022
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Mobile manipulation in robotics is challenging due to the need of solvin...
research
∙
10/12/2022
Near-Optimal Multi-Agent Learning for Safe Coverage Control
In multi-agent coverage control problems, agents navigate their environm...
research
∙
09/24/2022
Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control
From both an educational and research point of view, experiments on hard...
research
∙
10/15/2021
On-Policy Model Errors in Reinforcement Learning
Model-free reinforcement learning algorithms can compute policy gradient...
research
∙
10/06/2021
Model Learning and Contextual Controller Tuning for Autonomous Racing
Model predictive control has been widely used in the field of autonomous...
research
∙
03/08/2021
Design, Optimal Guidance and Control of a Low-cost Re-usable Electric Model Rocket
In the last decade, autonomous vertical take-off and landing (VTOL) vehi...
research
∙
11/18/2020
Cautious Bayesian Optimization for Efficient and Scalable Policy Search
Sample efficiency is one of the key factors when applying policy search ...
research
∙
08/13/2020
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Data availability has dramatically increased in recent years, driving mo...
research
∙
05/06/2020
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
This paper presents a framework for inverse learning of objective functi...
research
∙
02/07/2020
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
We consider the problem of robust optimization within the well-establish...
research
∙
01/21/2020
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection
Bayesian Optimization (BO) is an effective method for optimizing expensi...
research
∙
12/23/2019
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Established techniques for simulation and prediction with Gaussian proce...
research
∙
06/24/2019
Using Human Ratings for Feedback Control: A Supervised Learning Approach with Application to Rehabilitation Robotics
This paper presents a method for tailoring a parametric controller based...
research
∙
12/13/2018
Safe exploration of nonlinear dynamical systems: A predictive safety filter for reinforcement learning
Despite fast progress in Reinforcement Learning (RL), the transfer into ...
research
∙
03/22/2018