PhD Theses
- Dam, T. (2023). Sample Efficient Monte Carlo Tree Search for Robotics, Ph.D. Thesis.
- Flynn, H. (2023). PAC-Bayesian Bandit Algorithms With Guarantees, Ph.D. Thesis.
- Klink, P. (2023). Reinforcement Learning Curricula as Interpolations between Task Distributions, Ph.D. Thesis.
- Look, A. (2023). Deterministic Approximations for Deep State-Space Models, Ph.D. Thesis.
- Prasad, V.; (2023). Learning Human-Robot Interaction: A Case Study on Human-Robot Handshaking, Ph.D. Thesis.
- Urain, J. (2023). Deep Generative Models for Motion Planning and Control, Ph.D. Thesis.
- Abdulsamad, H. (2022). Statistical Machine Learning for Modeling and Control of Stochastic Structured Systems, Ph.D. Thesis.
- Becker-Ehmck, P. (2022). Latent State-Space Models for Control, Ph.D. Thesis.
- Belousov, B. (2022). On Optimal Behavior Under Uncertainty in Humans and Robots, Ph.D. Thesis.
- Cowen-Rivers, A. (2022). Pushing The Limits of Sample-Efficient Optimisation, Ph.D. Thesis.
- Arenz, O. (2021). Sample-Efficient I-Projections for Robot Learning, Ph.D. Thesis.
- Loeckel, S. (2021). Machine Learning for Modeling and Analyzing of Race Car Drivers, Ph.D. Thesis.
- Lutter, M. (2021). Inductive Biases for Machine Learning in Robotics and Control, Ph.D. Thesis.
- Muratore, F. (2021). Randomizing Physics Simulations for Robot Learning, Ph.D. Thesis.
- Tosatto, S. (2021). Off-Policy Reinforcement Learning for Robotics, PhD Thesis.
- Koert, D. (2020). Interactive Machine Learning for Assistive Robots, Ph.D. Thesis.
- Lampariello, R. (2020). Optimal Motion Planning for Object Interception and Grasping, Ph.D. Thesis.
- Tanneberg, D. (2020). Understand-Compute-Adapt: Neural Networks for Intelligent Agents, Ph.D. Thesis.
- Buechler, D. (2019). Robot Learning for Muscular Systems, Ph.D. Thesis.
- Ewerton, M. (2019). Bidirectional Human-Robot Learning: Imitation and Skill Improvement, PhD Thesis.
- Gebhardt, G.H.W. (2019). Using Mean Embeddings for State Estimation and Reinforcement Learning, PhD Thesis.
- Gomez-Gonzalez, S. (2019). Real Time Probabilistic Models for Robot Trajectories, Ph.D. Thesis.
- Parisi, S. (2019). Reinforcement Learning with Sparse and Multiple Rewards, PhD Thesis.
- Koc, O. (2018). Optimal Trajectory Generation and Learning Control for Robot Table Tennis, PhD Thesis.
- Lioutikov, R. (2018). Parsing Motion and Composing Behavior for Semi-Autonomous Manipulation, PhD Thesis.
- Veiga, F. (2018). Toward Dextrous In-Hand Manipulation through Tactile Sensing, PhD Thesis.
- Manschitz, S. (2017). Learning Sequential Skills for Robot Manipulation Tasks, PhD Thesis.
- Paraschos, A. (2017). Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives, PhD Thesis.
- Vinogradska, J. (2017). Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation, PhD Thesis.
- Calandra, R. (2016). Bayesian Modeling for Optimization and Control in Robotics, PhD Thesis.
- Daniel, C. (2016). Learning Hierarchical Policies from Human Feedback, PhD Thesis.
- Hoof, H.v. (2016). Machine Learning through Exploration for Perception-Driven Robotics, PhD Thesis.
- Kroemer, O. (2015). Machine Learning for Robot Grasping and Manipulation, PhD Thesis.
- Muelling, K. (2013). Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis, PhD Thesis.
- Wang, Z. (2013). Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Model, PhD Thesis.
- Kober, J. (2012). Learning Motor Skills: From Algorithms to Robot Experiments, PhD Thesis.
- Nguyen-Tuong, D (2011). Model Learning in Robot Control, PhD Thesis (Completed at IAS/Tuebingen before move to TU Darmstadt).