A complete list of publications can be found on my Google Scholar profile. The lists below are sorted in chronological order. My name is in bold, the students I supervise are underlined, and equal distribution is indicated by an asterisk (*) after the respective names.
2025
Semantically safe robot manipulation: from semantic scene understanding to motion safeguards
L. Brunke, Y. Zhang, R. Römer, J. Naimer, N. Staykov, S. Zhou, and A. P. Schoellig
accepted to the IEEE Robotics and Automation Letters (RA-L).
preprint | video | website
2024
Advancing reproducibility, benchmarks, and education with remote sim2real
S. Teetaert, W. Zhao, A. Loquercio, S. Zhou, L. Brunke, M. Schuck, W. Hoenig, J. Panerati, and A. P. Schoellig
accepted to the IEEE Robotics and Automation Magazine (RAM), 2024.
Practical considerations for discrete-time implementations of continuous-time control barrier function-based safety filters
L. Brunke, S. Zhou, M. Che, and A. P. Schoellig
in Proc. of the American Control Conference (ACC), 2024, pp. 272-278, doi: 10.23919/ACC60939.2024.10644713.
preprint | final version | talk
Is data all that matters? The role of control frequency for learning-based sampled-data control of uncertain systems
R. Römer, L. Brunke, S. Zhou, and A. P. Schoellig
in Proc. of the American Control Conference (ACC), 2024, pp. 1249-1255, doi: 10.23919/ACC60939.2024.10644546.
preprint | final version | talk
Control-barrier-aided teleoperation with Visual-Inertial SLAM for safe MAV navigation in complex environments
S. Zhou, S. Papatheodorou, S. Leutenegger, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 17836-17842, doi: 10.1109/ICRA57147.2024.10611280.
preprint | final version | video | talk
Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments
J. Qian, S. Zhou, N. J. Ren, V. Chatrath, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 11641-11648, doi: 10.1109/ICRA57147.2024.10610267.
preprint | final version | video
AMSwarmX: Safe swarm coordination in Complex environments via implicit non-convex decomposition of the obstacle-free space
V. K. Adajania, S. Zhou, A. K Singh, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 272-278, doi: 10.23919/ACC60939.2024.10644713.
preprint | final version | video
Optimized control invariance conditions for uncertain input-constrained nonlinear control systems
L. Brunke, S. Zhou, M. Che, and A. P. Schoellig
in IEEE Control Systems Letters, vol. 8, pp. 157-162, 2024, doi: 10.1109/LCSYS.2023.3344138.
preprint | final version | video | talk
Hierarchical task model predictive control for sequential mobile manipulation tasks
X. Du, S. Zhou, and A. P. Schoellig
in IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1270-1277, 2024, doi: 10.1109/LRA.2023.3342671.
preprint | final version | video
2023
What is the impact of releasing code with publications? Statistics from the machine learning, robotics, and control communities
S. Zhou, L. Brunke, A. Tao, A. W. Hall, F. P. Bejarano, J. Panerati, and A. P. Schoellig
in IEEE Control Systems Magazine (CSM), vol. 44, no. 4, pp. 38-46, 2024, doi: 10.1109/MCS.2024.3402888.
preprint | final version | workshop
AMSwarm: An alternating minimization approach for safe motion planning of quadrotor swarms in cluttered environments
V. K. Adajania, S. Zhou, A. K. Singh, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1421-1427, doi: 10.1109/ICRA48891.2023.10161063.
preprint | final version | code | video
2022
Barrier Bayesian linear regression: Online learning of control barrier conditions for safety-critical control of uncertain systems
L. Brunke*, S. Zhou*, and A. P. Schoellig
in Proc. of the Learning for Dynamics and Control Conference (CoRL), 2022, pp. 881-892.
preprint | final version
safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning in robotics
Z. Yuan, A. W. Hall, S. Zhou, L. Brunke, M. Greeff, J. Panerati, and A. P. Schoellig
in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11142-11149, 2022, doi: 10.1109/LRA.2022.3196132.
preprint | final version | code
Robust predictive output-feedback safety filter for uncertain nonlinear control systems
L. Brunke, S. Zhou and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2022, pp. 3051-3058, doi: 10.1109/CDC51059.2022.9992834.
preprint | final version
Bridging the model-reality gap with Lipschitz network adaptation
S. Zhou, K. Pereida, W. Zhao, and A. P. Schoellig
in the IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 1, pp. 642-649, 2022, doi: 10.1109/LRA.2021.3131698.
preprint | final version | video | talk
Fly out the window: Exploiting discrete-time flatness for fast vision-based multirotor flight
M. Greeff, S. Zhou, and A. P. Schoellig
in the IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 5023-5030, April 2022, doi: 10.1109/LRA.2022.3154008.
preprint | final version
2021
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, and A. P. Schoellig
in Proc. of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 7512-7519, doi: 10.1109/IROS51168.2021.9635857.
preprint | final version | code | website
RLO-MPC: Robust learning-based output feedback MPC for improving the performance of uncertain systems in iterative tasks
L. Brunke, S. Zhou and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2021, pp. 2183-2190, doi: 10.1109/CDC45484.2021.9682940.
preprint | final version | talk
Safe learning in robotics: from learning-based control to safe reinforcement learning
L. Brunke*, M. Greeff*, A. W. Hall*, Z. Yuan*, S. Zhou*, J. Panerati, and A. P. Schoellig
in Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, pp. 411-444, 2021, doi: 10.1146/annurev-control-042920-020211.
preprint | final version
To share or not to share? Performance guarantees and the asymmetric nature of cross-robot experience transfer
M. J. Sorocky, S. Zhou, and A. P. Schoellig
in IEEE Control Systems Letters, vol. 5, no. 3, pp. 923-928, 2021, doi: 10.1109/LCSYS.2020.3005886.
preprint | final version | video
2020
Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
in International Journal of Robotics Research (IJRR), vol. 39, no. 12, pp. 1397-1418, 2020, doi: 10.1177/0278364920953902.
preprint | final version | video
Experience selection using dynamics similarity for efficient multi-source transfer learning between robots
M. J. Sorocky*, S. Zhou*, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 2739-2745, doi: 10.1109/ICRA40945.2020.9196744.
preprint | final version | video | talk
2019
Active training trajectory generation for inverse dynamics model learning with deep neural networks
S. Zhou and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2019, pp. 1784-1790, doi: 10.1109/CDC40024.2019.9029973.
preprint | final version
Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking
S. Zhou, A. Sarabakha, E. Kayacan, M. K. Helwa, and A. P. Schoellig
in Proc. of the European Control Conference (ECC), 2019, pp. 1-8, doi: 10.23919/ECC.2019.8796140.
preprint | final version | video
2018
An inversion-based learning approach for improving impromptu trajectory tracking of robots with non-minimum phase dynamics
S. Zhou, M. K. Helwa, and A. P. Schoellig
in IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1663-1670, 2018, doi: 10.1109/LRA.2018.2801471.
preprint | final version
2017
Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2017, pp. 5201-5207, doi: 10.1109/CDC.2017.8264430.
preprint | final version | video
Extended Abstracts and Other Contributions
An analysis of the expressiveness of deep neural network architectures based on their Lipschitz constants
S. Zhou and A. P. Schoellig
preprint
Knowledge Transfer Between Robots with Online Learning for Enhancing Robot Performance in Impromptu Trajectory Tracking
S. Zhou, A. Sarabakha, E. Kayacan, M. K. Helwa, and A. P. Schoellig
presented at the ICRA 2019 Workshop on Resilient Robot Teams: Composing, Acting, and Learning
abstract | poster
Deep neural networks as add-on modules for high-accuracy impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
presented at the Conference on Robot Learning (CoRL) 2017
abstract | supplementary materials | talk
A comparison of probabilistic population code and sampling-based code in neural state estimations
S. Zhou
presented at the Conference on Cognitive Computational Neuroscience (CCN) 2017
abstract | poster