no code implementations • 18 Apr 2024 • Lukas Brunke, SiQi Zhou, Mingxuan Che, Angela P. Schoellig
In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero.
1 code implementation • 14 Mar 2024 • Ralf Römer, Lukas Brunke, SiQi Zhou, Angela P. Schoellig
While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance.
no code implementations • 6 Mar 2024 • SiQi Zhou, Ling Wang, Jie Liu, Jinshan Tang
However, there are large difference between the simulation results obtained by the crop models and the actual results, thus in this paper, we proposed to combine the simulation results with the collected crop data for data assimilation so that the accuracy of prediction will be improved.
no code implementations • 15 Dec 2023 • Lukas Brunke, SiQi Zhou, Mingxuan Che, Angela P. Schoellig
We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints.
1 code implementation • 19 Aug 2023 • SiQi Zhou, Lukas Brunke, Allen Tao, Adam W. Hall, Federico Pizarro Bejarano, Jacopo Panerati, Angela P. Schoellig
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community.
1 code implementation • 17 Dec 2022 • Lukas Brunke, SiQi Zhou, Angela P. Schoellig
Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties.
no code implementations • 7 Dec 2021 • SiQi Zhou, Karime Pereida, Wenda Zhao, Angela P. Schoellig
In particular, we present a learning-based model reference adaptation approach that makes a robot system, with possibly uncertain dynamics, behave as a predefined reference model.
no code implementations • 1 Oct 2021 • 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.
4 code implementations • 13 Sep 2021 • Zhaocong Yuan, Adam W. Hall, SiQi Zhou, Lukas Brunke, Melissa Greeff, Jacopo Panerati, Angela P. Schoellig
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 significant traction.
4 code implementations • 13 Aug 2021 • Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, SiQi Zhou, Jacopo Panerati, Angela P. Schoellig
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 learning communities.
2 code implementations • 3 Mar 2021 • Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, Angela P. Schoellig
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications.
no code implementations • 29 Mar 2020 • Michael J. Sorocky, Siqi Zhou, Angela P. Schoellig
We show that selecting experiences based on the proposed similarity metric effectively facilitates the learning of the target quadrotor, improving performance by 62% compared to a poorly selected experience.
no code implementations • 24 Dec 2019 • SiQi Zhou, Angela P. Schoellig
We consider this work to be a step towards understanding the expressive power of DNNs and towards designing appropriate deep architectures for practical applications such as system control.
no code implementations • 13 Sep 2017 • 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.