activity
20192022
most citedAn Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants

6 citations · 10 across the 6 of their papers we have counts for

collaborators

9 papers

cs.RO20223 cited

Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems

Lukas Brunke, Siqi Zhou, Angela P. Schoellig

In this work, we consider the problem of designing a safety filter for a nonlinear uncertain control system. Our goal is to augment an arbitrary controller with a safety filter suc…

eess.SY2021

RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks

Lukas Brunke, Siqi Zhou, Angela P. Schoellig

In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon opt…

cs.RO2021

Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight

Melissa Greeff, Siqi Zhou, Angela P. Schoellig

Current control design for fast vision-based flight tends to rely on high-rate, high-dimensional and perfect state estimation. This is challenging in real-world environments due to…

quant-ph2021

Tomographic Witnessing and Holographic Quantifying of Coherence

Bang-Hai Wang, Si-Qi Zhou, Zhihao Ma +1

The detection and quantification of quantum coherence play significant roles in quantum information processing. We present an efficient way of tomographic witnessing for both theor…

cs.RO2021

Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

Jacopo Panerati, Hehui Zheng, SiQi Zhou +3

Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulat…

cs.RO2020

To Share or Not to Share? Performance Guarantees and the Asymmetric Nature of Cross-Robot Experience Transfer

Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig

In the robotics literature, experience transfer has been proposed in different learning-based control frameworks to minimize the costs and risks associated with training robots. Wh…