User:RoboFemme/sandbox angela schoellig

Angela Schoellig is an Alexander von Humboldt Professor for Robotics and Artificial Intelligence at the Technical University of Munich. She is also an Associate Professor at the University of Toronto Institute for Aerospace Studies and a Faculty Member of the Vector Institute in Toronto. Angela conducts research at the intersection of robotics, controls, and machine learning. Her goal is to enhance the performance, safety, and autonomy of robots by enabling them to learn from past experiments and each other. In Canada, she has held a Canada Research Chair (Tier 2) in Machine Learning for Robotics and Control and a Canada CIFAR Chair in Artificial Intelligence. She has been a principal investigator of the NSERC Canadian Robotics Network. She is a recipient of the Robotics: Science and Systems Early Career Spotlight Award (2019), a Sloan Research Fellowship (2017), and an Ontario Early Researcher Award (2017). She is one of MIT Technology Review’s Innovators Under 35 (2017), a Canada Science Leadership Program Fellow (2014), and one of Robohub’s “25 women in robotics you need to know about (2013)”. Her team is the four-time winner of the North-American SAE AutoDrive Challenge (2018-21).

Education
Angela completed her PhD at ETH Zurich (2013) was awarded the ETH Medal and the Dimitris N. Chorafas Foundation Award. She holds both an M.Sc. in Engineering Cybernetics from the University of Stuttgart (2008) and an M.Sc. in Engineering Science and Mechanics from the Georgia Institute of Technology (2007).

Awards & Honors

 * MIT Technology Review’s 35 Innovators Under 35 (Technology Review article, University of Toronto news, CBC interview), 2017.
 * Sloan Research Fellowship, 2017
 * Ministry of Research, Innovation & Science Early Researcher Award, 2017.
 * Nominated for a Canada Research Chair Tier 2, 2017.
 * Connaught New Researcher Award, 2015.
 * MIT Enabling Society Tech Competition First Prize, 2015.
 * $1M Drones For Good Competition Finalist, 2015.
 * IEEE Control Systems Society (CSS) Video Clip Contest Finalist (with this video), 2014.
 * Best Robotics Paper Award at the Conference on Computer and Robot Vision (CRV) (with this paper), 2014.
 * Science Leadership Program Fellow, 2014.
 * Dimitris N. Chorafas Foundation Prize (as one of 35 worldwide, for my PhD thesis), 2013.
 * ETH Medal (awarded to the top 8% of Ph.D. dissertations at ETH Zurich, for my PhD thesis), 2013.
 * Named one of “25 women in robotics you need to know about” by Robohub.org, 2013.
 * Professor-Peter-Sagirow Award (for outstanding performance in the undergraduate program Engineering Cybernetics, awarded to the top two students out of 70), 2005.

2022

 * Safe-control-gym: a unified benchmark suite for safe learning-based control and reinforcement learning in robotics, Z. Yuan, A. W. Hall, S. Zhou, M. G. Lukas Brunke and, J. Panerati, and A. P. Schoellig, IEEE Robotics and Automation Letters, 2022. Accepted.
 * Are we ready for radar to replace lidar in all-weather mapping and localization?, K. Burnett, Y. Wu, D. J. Yoon, A. P. Schoellig, and T. D. Barfoot, IEEE Robotics and Automation Letters, vol. 4, iss. 7, p. 10328–10335, 2022.
 * Min-max vertex cycle covers with connectivity constraints for multi-robot patrolling, J. Scherer, A. P. Schoellig, and B. Rinner, IEEE Robotics and Automation Letters, vol. 4, iss. 7, p. 10152–10159, 2022.
 * Boreas: a multi-season autonomous driving dataset, K. Burnett, D. J. Yoon, Y. Wu, A. Z. Li, H. Zhang, S. Lu, J. Qian, W. Tseng, A. Lambert, K. Y. K. Leung, A. P. Schoellig, and T. D. Barfoot, International Journal of Robotics Research, 2022. Submitted.
 * Finding the right place: sensor placement for UWB time difference of arrival localization in cluttered indoor environments, W. Zhao, A. Goudar, and A. P. Schoellig, IEEE Robotics and Automation Letters, 2022.
 * 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, Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, iss. 1, 2022.
 * Fly out the window: exploiting discrete-time flatness for fast vision-based multirotor flight, M. Greeff, S. Zhou, and A. P. Schoellig, IEEE Robotics and Automation Letters, vol. 7, iss. 2, p. 5023–5030, 2022.
 * Bridging the model-reality gap with Lipschitz network adaptation, S. Zhou, K. Pereida, W. Zhao, and A. P. Schoellig, IEEE Robotics and Automation Letters, vol. 7, iss. 1, p. 642–649, 2022.
 * Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an on-manifold extended Kalman filter, N. Kayhani, W. Zhao, B. McCabe, and A. P. Schoellig, Automation in Construction, vol. 135, p. 104112, 2022.
 * Gaussian variational inference with covariance constraints applied to range-only localization, A. Goudar, W. Zhao, T. D. Barfoot, and A. P. Schoellig, in Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. Accepted.
 * POCD: probabilistic object-level change detection and volumetric mapping in semi-static scenes, J. Qian, V. Chatrath, J. Yang, J. Servos, A. Schoellig, and S. L. Waslander, in Proc. of Robotics: Science and Systems (RSS), 2022. Accepted.
 * Fusion of machine learning and mpc under uncertainty: what advances are on the horizon?, A. Mesbah, K. P. Wabersich, A. P. Schoellig, M. N. Zeilinger, S. Lucia, T. A. Badgwell, and J. A. Paulson, in Proc. of the American Control Conference (ACC), 2022. Accepted.
 * 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 4th Annual Learning for Dynamics and Control Conference, 2022, p. 881–892.
 * Stochastic modeling of tag installation error for robust on-manifold tag-based visual-inertial localization, N. Kayhani, B. McCabe, and A. P. Schoellig, in Proc. of the Canadian Society of Civil Engineering Annual Conference (CSCE), 2022. Accepted.

2021

 * Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics, F. Berkenkamp, A. Krause, and A. P. Schoellig, Machine Learning, 2021.
 * Robust adaptive model predictive control for guaranteed fast and accurate stabilization in the presence of model errors, K. Pereida, L. Brunke, and A. P. Schoellig, International Journal of Robust and Nonlinear Control, vol. 31, iss. 18, p. 8750–8784, 2021.
 * A deep learning approach for rock fragmentation analysis, T. Bamford, K. Esmaeili, and A. P. Schoellig, International Journal of Rock Mechanics and Mining Sciences, vol. 145, p. 104839, 2021.
 * Meta learning with paired forward and inverse models for efficient receding horizon control, C. D. McKinnon and A. P. Schoellig, IEEE Robotics and Automation Letters, vol. 6, iss. 2, p. 3240–3247, 2021.
 * Do we need to compensate for motion distortion and Doppler effects in spinning radar navigation?, K. Burnett, A. P. Schoellig, and T. D. Barfoot, IEEE Robotics and Automation Letters, vol. 6, iss. 2, p. 771–778, 2021.
 * Learning-based bias correction for time difference of arrival ultra-wideband localization of resource-constrained mobile robots, W. Zhao, J. Panerati, and A. P. Schoellig, IEEE Robotics and Automation Letters, vol. 6, iss. 2, p. 3639–3646, 2021.
 * Exploiting differential flatness for robust learning-based tracking control using Gaussian processes, M. Greeff and A. Schoellig, IEEE Control Systems Letters, vol. 5, iss. 4, p. 1121–1126, 2021.
 * Self-calibration of the offset between GPS and semantic map frames for robust localization, W. Tseng, A. P. Schoellig, and T. D. Barfoot, in Proc. of the Conference on Robots and Vision (CRV), 2021, p. 173–180.
 * Radar odometry combining probabilistic estimation and unsupervised feature learning, K. Burnett, D. J. Yoon, A. P. Schoellig, and T. D. Barfoot, in Proc. of Robotics: Science and Systems (RSS), 2021.
 * 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.
 * Learning a stability filter for uncertain differentially flat systems using Gaussian processes, M. Greeff, A. W. Hall, and A. P. Schoellig, in Proc. of the IEEE Conference on Decision and Control (CDC), 2021, p. 789–794.
 * Online spatio-temporal calibration of tightly-coupled ultrawideband-aided inertial localization, A. Goudar and A. P. Schoellig, in Proc. of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2021, p. 1161–1168.
 * Mobile manipulation in unknown environments with differential inverse kinematics control, A. Heins, M. Jakob, and A. P. Schoellig, in Proc. of the Conference on Robots and Vision (CRV), 2021, p. 64–71.
 * 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, p. 7512–7519.

Affiliations

 * Associate Director of the Centre for Aerial Robotics Research and Education (CARRE), since 2015.
 * Principal Faculty Advisor for the University of Toronto’s SAE/GM AutoDrive Challenge team competing in a 3-year self-driving competition, 2017-2020.
 * Faculty Advisor of the University of Toronto Aerospace Team (UTAT), since 2015.
 * Affiliated with the University of Toronto’s Institute for Robotics and Mechatronics and the Lassonde Institute for Mining, since 2014.