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SiQi Zhou
SiQi Zhou
University of Toronto Institute for Aerospace Studies
Verified email at robotics.utias.utoronto.ca - Homepage
Title
Cited by
Cited by
Year
Safe learning in robotics: From learning-based control to safe reinforcement learning
L Brunke, M Greeff, AW Hall, Z Yuan, S Zhou, J Panerati, AP Schoellig
Annual Review of Control, Robotics, and Autonomous Systems 5, 411-444, 2022
452022
Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking
S Zhou, MK Helwa, AP Schoellig
2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 5201-5207, 2017
392017
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
J Panerati, H Zheng, SQ Zhou, J Xu, A Prorok, AP Schoellig
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
222021
An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics
S Zhou, MK Helwa, AP Schoellig
IEEE Robotics and Automation Letters (RA-L) 3 (3), pp. 1663 - 1670, 2017
142017
Experience selection using dynamics similarity for efficient multi-source transfer learning between robots
MJ Sorocky, S Zhou, AP Schoellig
2020 IEEE International Conference on Robotics and Automation (ICRA), 2739-2745, 2020
102020
Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking
S Zhou, MK Helwa, AP Schoellig, A Sarabakha, E Kayacan
2019 18th European Control Conference (ECC), 1-8, 2019
102019
An analysis of the expressiveness of deep neural network architectures based on their lipschitz constants
SQ Zhou, AP Schoellig
arXiv preprint arXiv:1912.11511, 2019
62019
Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
S Zhou, MK Helwa, AP Schoellig
The International Journal of Robotics Research 39 (12), 1397-1418, 2020
52020
Active training trajectory generation for inverse dynamics model learning with deep neural networks
S Zhou, AP Schoellig
2019 IEEE 58th Conference on Decision and Control (CDC), 1784-1790, 2019
32019
To share or not to share? performance guarantees and the asymmetric nature of cross-robot experience transfer
MJ Sorocky, S Zhou, AP Schoellig
IEEE Control Systems Letters 5 (3), 923-928, 2020
22020
Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight
M Greeff, SQ Zhou, AP Schoellig
IEEE Robotics and Automation Letters 7 (2), 5023-5030, 2022
12022
RLO-MPC: Robust learning-based output feedback MPC for improving the performance of uncertain systems in iterative tasks
L Brunke, S Zhou, AP Schoellig
2021 60th IEEE Conference on Decision and Control (CDC), 2183-2190, 2021
12021
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning
Z Yuan, AW Hall, S Zhou, L Brunke, M Greeff, J Panerati, AP Schoellig
arXiv preprint arXiv:2109.06325, 2021
12021
Barrier Bayesian Linear Regression: Online Learning of Control Barrier Conditions for Safety-Critical Control of Uncertain Systems
L Brunke, S Zhou, AP Schoellig
Learning for Dynamics and Control Conference, 881-892, 2022
2022
Bridging the Model-Reality Gap With Lipschitz Network Adaptation
S Zhou, K Pereida, W Zhao, AP Schoellig
IEEE Robotics and Automation Letters 7 (1), 642-649, 2021
2021
A Comparison of Probabilistic Population Code and Sampling-based Code in Neural Estimations of Time-Varying Quantities
S Zhou
Cognitive Computational Neuroscience (CCN) 2017, 2017
2017
Knowledge Transfer Between Robots with Online Learning for Enhancing Robot Performance in Impromptu Trajectory Tracking
S Zhou, A Sarabakha, E Kayacan, MK Helwa, AP Schoellig
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