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Where did I leave my glasses? Open-vocabulary semantic exploration in real-world semi-static environments
B. Bogenberger · O. Harrison · D. O. Dahanaggamaarachchi · L. Brunke · J. Qian · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2026
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Preventing unconstrained CBF safety filters caused by incorrect relative degree assumptions
L. Brunke · S. Zhou · A. P. Schoellig
IEEE Transactions on Automatic Control (TAC), 2026
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SM2ITH: safe mobile manipulation with interactive human prediction via task-hierarchical bilevel model predictive control
D'Orazio · S. Sepehr · X. Du · S. Zhou · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2026
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SwarmGPT: combining large language models with safe motion planning for drone swarm choreography
M. Schuck · D. O. Dahanaggamaarachchi · B. Sprenger · V. Vyas · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2025
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Semantically safe robot manipulation: from semantic scene understanding to motion safeguards
L. Brunke · Y. Zhang · R. Römer · J. Naimer · N. Staykov · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2025
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Advancing reproducibility, benchmarks, and education with remote sim2real
S. Teetaert · W. Zhao · A. Loquercio · S. Zhou · L. Brunke · M. Schuck · W. Hoenig · J. Panerati · A. P. Schoellig
IEEE Robotics and Automation Magazine (RAM), 2025
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Safe multi-agent reinforcement learning for behavior-based cooperative navigation
M. Elnagdi · S. Pan · N. Dengler · S. Zhou · A. P. Schoellig · M. Bennewitz
IEEE Robotics and Automation Letters (RA-L), 2025
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Perception-based hierarchical-task MPC for sequential mobile manipulation in unstructured semi-static environments
X. Du* · J. Qian* · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2025 (under review)
on Jul. 27, 2025, submission #25-3280. [
pdf]
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Optimized control invariance conditions for uncertain input-constrained nonlinear control systems
L. Brunke · S. Zhou · M. Che · A. P. Schoellig
IEEE Control Systems Letters (L-CSS), 2024
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Hierarchical task model predictive control for sequential mobile manipulation tasks
X. Du · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2024
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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. Pizarro Bejarano · J. Panerati · A. P. Schoellig
IEEE Control Systems Magazine (CSM), 2024
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Control-barrier-aided teleoperation with visual-inertial SLAM for safe MAV navigation in complex environments
S. Zhou · S. Papatheodorou · S. Leutenegger · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024
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Closing the perception-action loop for semantically safe navigation in semi-static environments
J. Qian · S. Zhou · N. J. Ren · V. Chatrath · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024
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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 · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2024
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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 · A. P. Schoellig
Proc. of the American Control Conference (ACC), 2024
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Practical considerations for discrete-time implementations of continuous-time control barrier function-based safety filters
L. Brunke · S. Zhou · M. Che · A. P. Schoellig
Proc. of the American Control Conference (ACC), 2024
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Semantically safe robot manipulation: from semantic scene understanding to motion safeguard
L. Brunke · Y. Zhang · R. Römer · J. Naimer · S. Zhou · A. P. Schoellig
Proc. of the Conference on Neural Information Processing Systems (NeurIPS) Workshop on Open-World Agents, 2024
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AMSwarm: an alternating minimization approach for safe motion planning of quadrotor swarms in cluttered environments
V. K. Adajania · S. Zhou · A. K. Singh · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2023
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Swarm-GPT: Combining large language models with safe motion planning for robot choreography design
A. Jiao · T. P. Patel · S. Khurana · A. Korol · L. Brunke · V. K. Adajania · U. Culha · S. Zhou · A. P. Schoellig
Proc. of the Conference on Neural Information Processing Systems (NeurIPS) Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models, 2023
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Bridging the model-reality gap with Lipschitz network adaptation
S. Zhou · K. Pereida · W. Zhao · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2022
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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 · A. P. Schoellig
Annual Review of Control, Robotics, and Autonomous Systems, 2022
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safe-control-gym: a unified benchmark suite for safe learning-based control and reinforcement learning
Z. Yuan · A. W. Hall · S. Zhou · L. Brunke · M. Greeff · J. Panerati · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2022
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Fly out the window: exploiting discrete-time flatness for fast vision-based multirotor flight
M. Greeff · S. Zhou · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2022
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Robust predictive output-feedback safety filter for uncertain nonlinear control systems
L. Brunke · S. Zhou · A. P. Schoellig
Proc. of the IEEE Conference on Decision and Control (CDC), 2022
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Barrier Bayesian linear regression: online learning of control barrier conditions for safety-critical control of uncertain systems
L. Brunke* · S. Zhou* · A. P. Schoellig
Proc. of the Annual Learning for Dynamics and Control Conference (L4DC), 2022
pp. 881-892, Extended abstract. [
pdf]
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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 · A. P. Schoellig
Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
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RLO-MPC: Robust learning-based output feedback MPC for improving the performance of uncertain systems in iterative tasks
L. Brunke · S. Zhou · A. P. Schoellig
Proc. of the IEEE Conference on Decision and Control (CDC), 2021
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Deep neural networks as add-on modules for enhancing robot performance in impromptu trajectory tracking
S. Zhou · M. K. Helwa · A. P. Schoellig
International Journal of Robotics Research (IJRR), 2020
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To share or not to share? Performance guarantees and the asymmetric nature of cross-robot experience transfer
M. J. Sorocky · S. Zhou · A. P. Schoellig
IEEE Control Systems Letters (L-CSS), 2020
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Experience selection using dynamics similarity for efficient multi-source transfer learning between robots
M. J. Sorocky* · S. Zhou* · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020
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An analysis of the expressiveness of deep neural network architectures based on their lipschitz constants
S. Zhou · A. P. Schoellig
arXiv Preprint, 2020
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Active training trajectory generation for inverse dynamics model learning with deep neural networks
S. Zhou · A. P. Schoellig
Proc. of the IEEE Conference on Decision and Control (CDC), 2019
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Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking
S. Zhou · A. Sarabakha · E. Kayacan · M. K. Helwa · A. P. Schoellig
Proc. of the European Control Conference (ECC), 2019
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Knowledge transfer between robots with online learning for enhancing robot performance in impromptu trajectory tracking
S. Zhou · A. Sarabakha · E. Kayacan · M. K. Helwa · A. P. Schoellig
Proc. of the IEEE International Conference on Robotics and Automation (ICRA) Resilient Robot Teams Workshop, 2019
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An inversion-based learning approach for improving impromptu trajectory tracking of robots with non-minimum phase dynamics
S. Zhou · M. K. Helwa · A. P. Schoellig
IEEE Robotics and Automation Letters (RA-L), 2018
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Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking
S. Zhou · M. K. Helwa · A. P. Schoellig
Proc. of the IEEE Conference on Decision and Control (CDC), 2017
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Deep neural networks as add-on modules for high-accuracy impromptu trajectory tracking
S. Zhou · M. K. Helwa · A. P. Schoellig
Proc. of the Conference on Robot Learning (CoRL), 2017
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A comparison of probabilistic population code and sampling-based code in neural state
estimations
S. Zhou
Proc. of the Conference on Cognitive Computational Neuroscience (CCN), 2017