28 citations · 68 across the 8 of their papers we have counts for
10 papers
Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking
Siqi Zhou, Andriy Sarabakha, Erdal Kayacan +2
In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrot…
Estimation-Based Model Predictive Control for Automatic Crosswind Stabilization of Hybrid Aerial Vehicles
Mohamed K. Helwa, Adrian Esser, Angela P. Schoellig
In this paper, we study the control design of an automatic crosswind stabilization system for a novel, buoyantly-assisted aerial transportation vehicle. This vehicle has several ad…
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems
Mohamed K. Helwa, Adam Heins, Angela P. Schoellig
Lagrangian systems represent a wide range of robotic systems, including manipulators, wheeled and legged robots, and quadrotors. Inverse dynamics control and feedforward linearizat…
Data-Efficient Multirobot, Multitask Transfer Learning for Trajectory Tracking
Karime Pereida, Mohamed K. Helwa, Angela P. Schoellig
Transfer learning has the potential to reduce the burden of data collection and to decrease the unavoidable risks of the training phase. In this letter, we introduce a multirobot,…
An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics
Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig
This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics. Inversion-based feedfor…
Multi-Robot Transfer Learning: A Dynamical System Perspective
Mohamed K. Helwa, Angela P. Schoellig
Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of trainin…
Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking
Siqi Zhou, Mohamed K. Helwa, Angela P. Schoellig
This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories. The DNNs…
On the Control of Affine Systems with Safety Constraints: Relaxed In-Block Controllability
Mohamed K. Helwa, Peter E. Caines
We consider affine systems defined on polytopes and study the cases where the systems are not in-block controllable with respect to the given polytopes. That are the cases in which…
On the Construction of Safe Controllable Regions for Affine Systems with Applications to Robotics
Mohamed K. Helwa, Angela P. Schoellig
This paper studies the problem of constructing in-block controllable (IBC) regions for affine systems. That is, we are concerned with constructing regions in the state space of aff…
Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors
Qiyang Li, Jingxing Qian, Zining Zhu +3
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers,…