25 citations · 53 across the 18 of their papers we have counts for
18 papers
A Remote Sim2real Aerial Competition: Fostering Reproducibility and Solutions' Diversity in Robotics Challenges
Spencer Teetaert, Wenda Zhao, Niu Xinyuan +21
Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportu…
What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities
Siqi Zhou, Lukas Brunke, Allen Tao +4
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science dev…
AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments
Vivek K. Adajania, Siqi Zhou, Arun Kumar Singh +1
This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean dis…
Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing n…
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…
Bridging the Model-Reality Gap with Lipschitz Network Adaptation
Siqi Zhou, Karime Pereida, Wenda Zhao +1
As robots venture into the real world, they are subject to unmodeled dynamics and disturbances. Traditional model-based control approaches have been proven successful in relatively…
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…
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…
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
Zhaocong Yuan, Adam W. Hall, Siqi Zhou +4
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained…
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke, Melissa Greeff, Adam W. Hall +4
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learni…
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…
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…
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…
Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots
Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig
In the robotics literature, different knowledge transfer approaches have been proposed to leverage the experience from a source task or robot -- real or virtual -- to accelerate th…
An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants
SiQi Zhou, Angela P. Schoellig
Deep neural networks (DNNs) have emerged as a popular mathematical tool for function approximation due to their capability of modelling highly nonlinear functions. Their applicatio…
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…
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…
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…