| 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 | 318 | 2022 |
| 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 | 91 | 2021 |
| 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 | 45 | 2017 |
| 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 | 22* | 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 | 19 | 2020 |
| 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 | 19 | 2017 |
| 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 | 13 | 2019 |
| 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 | 11 | 2022 |
| 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 | 8 | 2019 |
| 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 | 7 | 2021 |
| 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 | 7 | 2020 |
| 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 | 6 | 2019 |
| 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 | 5 | 2020 |
| 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 | 4 | 2022 |
| 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 | 4 | 2021 |
| AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments VK Adajania, S Zhou, AK Singh, AP Schoellig arXiv preprint arXiv:2303.04856, 2023 | 3 | 2023 |
| 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 | 3 | 2022 |
| AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space VK Adajania, S Zhou, AK Singh, AP Schoellig arXiv preprint arXiv:2310.09195, 2023 | | 2023 |
| A Remote Sim2real Aerial Competition: Fostering Reproducibility and Solutions' Diversity in Robotics Challenges S Teetaert, W Zhao, N Xinyuan, H Zahir, H Leong, M Hidalgo, G Puga, ... arXiv preprint arXiv:2308.16743, 2023 | | 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, AW Hall, FP Bejarano, J Panerati, AP Schoellig arXiv preprint arXiv:2308.10008, 2023 | | 2023 |
Angela SchoelligProfessor of Robotics, Technical University of Munich and University of TorontoVerified email at tum.de
