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SiQi Zhou
SiQi Zhou
Technical University of Munich
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 (1), 411-444, 2022
5242022
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
J Panerati, H Zheng, S Zhou, J Xu, A Prorok, AP Schoellig
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
1452021
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
472017
Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics
Z Yuan, AW Hall, S Zhou, L Brunke, M Greeff, J Panerati, AP Schoellig
IEEE Robotics and Automation Letters 7 (4), 11142-11149, 2022
39*2022
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
262020
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
202017
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
192019
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
162022
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
112021
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
102020
An analysis of the expressiveness of deep neural network architectures based on their lipschitz constants
S Zhou, AP Schoellig
arXiv preprint arXiv:1912.11511, 2019
92019
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
82020
Amswarm: An alternating minimization approach for safe motion planning of quadrotor swarms in cluttered environments
VK Adajania, S Zhou, AK Singh, AP Schoellig
2023 IEEE International Conference on Robotics and Automation (ICRA), 1421-1427, 2023
72023
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
72019
Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight
M Greeff, S Zhou, AP Schoellig
IEEE Robotics and Automation Letters 7 (2), 5023-5030, 2022
52022
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
52021
Robust predictive output-feedback safety filter for uncertain nonlinear control systems
L Brunke, S Zhou, AP Schoellig
2022 IEEE 61st Conference on Decision and Control (CDC), 3051-3058, 2022
42022
Swarm-gpt: Combining large language models with safe motion planning for robot choreography design
A Jiao, TP Patel, S Khurana, AM Korol, L Brunke, VK Adajania, U Culha, ...
arXiv preprint arXiv:2312.01059, 2023
32023
Safe multi-agent reinforcement learning for formation control without individual reference targets
M Dawood, S Pan, N Dengler, S Zhou, AP Schoellig, M Bennewitz
arXiv preprint arXiv:2312.12861, 2023
22023
Optimized control invariance conditions for uncertain input-constrained nonlinear control systems
L Brunke, S Zhou, M Che, AP Schoellig
IEEE Control Systems Letters, 2023
12023
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