| Safe model-based reinforcement learning with stability guarantees F Berkenkamp, M Turchetta, A Schoellig, A Krause Advances in Neural Information Processing Systems, 908-918, 2017 | 868 | 2017 |
| A simple learning strategy for high-speed quadrocopter multi-flips S Lupashin, A Schöllig, M Sherback, R D'Andrea Robotics and Automation (ICRA), 2010 IEEE International Conference on, 1642-1648, 2010 | 448 | 2010 |
| Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach F Augugliaro, AP Schoellig, R D'Andrea 2012 IEEE/RSJ international conference on Intelligent Robots and Systems …, 2012 | 346 | 2012 |
| Learning-Based Nonlinear Model Predictive Control to Improve Vision-Based Mobile Robot Path-Tracking in Challenging Outdoor Environments CJ Ostafew, AP Schoellig, TD Barfoot IEEE International Conference on Robotics and Automation (ICRA), 4029–4036, 2014 | 319* | 2014 |
| 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, 2021 | 316 | 2021 |
| Safe controller optimization for quadrotors with Gaussian processes F Berkenkamp, AP Schoellig, A Krause 2016 IEEE International Conference on Robotics and Automation (ICRA), 491-496, 2016 | 307 | 2016 |
| A platform for aerial robotics research and demonstration: The Flying Machine Arena S Lupashin, M Hehn, MW Mueller, AP Schoellig, M Sherback, R D’Andrea Mechatronics 24 (1), 41-54, 2014 | 281 | 2014 |
| Optimizing a drone network to deliver automated external defibrillators. JJ Boutilier, SC Brooks, A Janmohamed, A Byers, JE Buick, C Zhan, ... Circulation 135 (25), 2454-2465, 2017 | 241 | 2017 |
| Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking CJ Ostafew, AP Schoellig, TD Barfoot The International Journal of Robotics Research 35 (13), 1547-1563, 2016 | 241 | 2016 |
| Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics F Berkenkamp, A Krause, AP Schoellig Machine Learning, 1-35, 2021 | 221 | 2021 |
| Safe learning of regions of attraction for uncertain, nonlinear systems with gaussian processes F Berkenkamp, R Moriconi, AP Schoellig, A Krause 2016 IEEE 55th Conference on Decision and Control (CDC), 4661-4666, 2016 | 212 | 2016 |
| Learning‐based Nonlinear Model Predictive Control to Improve Vision‐based Mobile Robot Path Tracking CJ Ostafew, AP Schoellig, TD Barfoot, J Collier Journal of Field Robotics 33 (1), 133-152, 2016 | 186 | 2016 |
| Optimization-based iterative learning for precise quadrocopter trajectory tracking AP Schoellig, FL Mueller, R D’Andrea Autonomous Robots 33 (1-2), 103-127, 2012 | 183 | 2012 |
| Safe and robust learning control with Gaussian processes F Berkenkamp, AP Schoellig Proc. of the European Control Conference (ECC), 2015 | 175 | 2015 |
| Application-driven design of aerial communication networks T Andre, KA Hummel, AP Schoellig, E Yanmaz, M Asadpour, C Bettstetter, ... IEEE Communications Magazine 52 (5), 129-137, 2014 | 168 | 2014 |
| Online trajectory generation with distributed model predictive control for multi-robot motion planning CE Luis, M Vukosavljev, AP Schoellig IEEE Robotics and Automation Letters 5 (2), 604-611, 2020 | 154 | 2020 |
| Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with bayesian optimization A Marco, F Berkenkamp, P Hennig, AP Schoellig, A Krause, S Schaal, ... 2017 IEEE International Conference on Robotics and Automation (ICRA), 1557-1563, 2017 | 141 | 2017 |
| Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control CE Luis, AP Schoellig IEEE Robotics and Automation Letters 4 (2), 375-382, 2019 | 105 | 2019 |
| Deep neural networks for improved, impromptu trajectory tracking of quadrotors Q Li, J Qian, Z Zhu, X Bao, MK Helwa, AP Schoellig Robotics and Automation (ICRA), 2017 IEEE International Conference on, 5183-5189, 2017 | 100 | 2017 |
| 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 | 91 | 2021 |
Timothy D BarfootProfessor, University of TorontoVerified email at utoronto.ca
