Abschlussarbeiten aka Theses Main
Offered Master- and Bachelors Theses
We are always searching for good students and have many good opportunities. However, we are also open to suggestions by students as long as they touch the topics relevant to intelligent systems.
> Click here for Offered Topics / Angebotene Themen <
Note: These are only for TU Darmstadt students. Please contact Jan Peters ( mail@jan-peters.net) or any other Team Member for more information.
Ongoing
Ongoing B.Sc. and M.Sc. Theses / B.Sc. und M.Sc. Themen in Bearbeitung
| Student | Advisor | Type | Topic |
|---|---|---|---|
| Jonas Sperling | Riad Akrour | M.Sc. | Intrinsically Motivated Tactile In-Hand Manipulation |
| Patrick Krabbe | Jan Peters | M.Sc. | Learning Riemannian Movement Primitives for Manipulation |
| Alexander Klein | Jan Peters, Andras Kupcsik | M.Sc. | Learning Robot Grasping of Industrial Work Pieces using Dense Object Descriptors |
| Moritz Schramm | Dorothea Koert | B.Sc. | Development of an Interactive Gesture Recognition System for an Assistive Robot |
| Zhenhui Zhou | Puze Liu, Julen Urain De Jesus | M.Sc. | Model-Based Policy Search in Multi-Arm Bandit problem |
| Mattis Kaemmerer | Joao Carvalho | M.Sc. | Measure-Valued Derivatives for Machine Learning |
| David Rother | Fabio Muratore, Thomas Weisswange | M.Sc. | Reinforcement Learning inDecentralized Multi-GoalMulti-Agent Settings |
| Hanyu Sun | Julen Urain De Jesus | M.Sc. | Can we improve time-series classification with Inverse Reinforcement Learning? |
| Lanmiao Liu | Julen Urain De Jesus | M.Sc. | Detection and Prediction of Human Gestures by Probabilistic Modelling |
| Jianhang He | Puze Liu | M.Sc. | Imitation Learning with Energy Based Models |
| Daniel Palenicek | Michael Lutter | M.Sc. | Model Based Reinforcement Learning |
| Yanhua Zhang | Tuan Dam, Pascal Klink | M.Sc. | Memory Representations for Partially Observable Reinforcement Learning |
| Cedric Derstroff | Tuan Dam, Georgia Chalvatzaki, Joni Pajarinen | M.Sc. | Memory Representations for Partially Observable Reinforcement Learning |
| Max Niessing | Georgia Chalvatzaki | M.Sc. | Learning latent object representations for grasp generation with generative-adversarial models |
| Haoyi Yang | Pascal Klink | M.Sc. | Variational Inference for Curriculum Reinforcement Learning |
| Yannick Eich | Hany Abdulsamad, Joe Watson | M.Sc. | Distributionally Robust Optimization for Hybrid Systems |
| Yannik Frisch | Georgia Chalvatzaki | M.Sc. | Self-supervised Visual Imitation Learning |
| Jan Schneider | Georgia Chalvatzaki, Carlo D'Eramo | M.Sc. | Visual Model-Predictive Actor-Critic Reinforcement Learning |
| Daljeet Nandha | Georgia Chalvatzaki | M.Sc. | Building Task Plans from Robot Knowledge Graphs |
| Jihao Andreas Lin | Joe Watson,Pascal Klink | M.Sc. | Functional Variational Inference for Bayesian Neural Networks |
| Mohammed Amin Ali | Joe Watson | B.Sc. | An Educational Framework for Robot Learning |
| Martina Gassen | Vignesh Prasad, Dorothea Koert | B.Sc. | Learning a library of Physical Interactions for Social Robots |
| Lukas Schneider | Tuan Dam, Carlo DEramo | B.Sc. | Distributional Monte-Carlo Tree Search |
| Frederik Wegner | Boris Belousov, Niklas Funk | M.Sc. | Learning Vision-Based Tactile Representations for Robotic Architectural Assembly |
| Tim Schneider | Boris Belousov, Hany Abdulsamad | M.Sc. | Tactile Active Inference |
| Theo Gruner | Fabio Muratore, Boris Belousov | M.Sc. | Wasserstein-Optimal Distributional System Identification for Domain Randomization |
| Hassan Wahba | Tianyu Ren | B.Sc. | Deep Object-centered Heuristic for Robot Planning |
| Jule Bredgen | Dorothea Koert | M.Sc. | The Relation between Social Interaction And Intrinsic Motivation in Reinforcement Learning |
Ongoing IP Projects
| Topic | Students | Adviser |
|---|---|---|
| Self-Paced Reinforcement Learning for Sim-to-Real | Fabian Damken, Heiko Carrasco | Fabio Muratore |
| Policy Distillation for Sim-to-Real | Benedikt Hahner, Julien Brosseit | Fabio Muratore |
| Neural Posterior System Identification | Theo Gruner, Florian Wiese | Fabio Muratore, Boris Belousov |
| Syntethic Dataset generation for Articulation prediction | Jiawei Huang, Niklas Babendererde | Julen Urain, Puze Liu |
| Guided Dimensionality Reduction for Black-Box Optimization | Marius Memmel | Puze Liu, Davide Tateo |
| Learning Laplacian Representations for continuous MCTS | Daniel Mansfeld, Alex Ruffini | Tuan Dam, Georgia Chalvatzaki, Carlo D'Eramo |
| Object Tracking using Depth Carmera | Leon Magnus, Svenja Menzenbach, Max Siebenborn | Niklas Funk, Boris Belousov, Georgia Chalvatzaki |
| GNNs for Robotic Manipulation | Fabio d'Aquino Hilt, Jan Kolf, Christian Weiland | Joao Carvalho, Samuele Tosatto |
| Benchmarking advances in MCTS in Go and Chess | Sijun Kwon, Lukas Schneider | Tuan Dam, Carlo D'eramo |
| Visual model-based reinforcement learning for robotic tasks | Rickmer Krohn | Georgia Chalvatzaki, Carlo D'eramo |
Completed
Only TU Darmstadt Theses and the Theses of IAS members from before the move to TU Darmstadt are listed.
Completed PhD Theses
Arenz, O. (2021). Sample-Efficient I-Projections for Robot Learning, Ph.D. Thesis, TU Darmstadt.
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Tosatto, S. (2021). Off-Policy Reinforcement Learning for Robotics, PhD Thesis.
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Koert, D. (2020). Interactive Machine Learning for Assistive Robots, Ph.D. Thesis.
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Lampariello, R. (2020). Optimal Motion Planning for Object Interception and Grasping, Ph.D. Thesis.
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Tanneberg, D. (2020). Understand-Compute-Adapt: Neural Networks for Intelligent Agents, Ph.D. Thesis.
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Buechler, D. (2019). Robot Learning for Muscular Systems, Ph.D. Thesis.
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Ewerton, M. (2019). Bidirectional Human-Robot Learning: Imitation and Skill Improvement, PhD Thesis.
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Gebhardt, G.H.W. (2019). Using Mean Embeddings for State Estimation and Reinforcement Learning, PhD Thesis.
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Gomez-Gonzalez, S. (2019). Real Time Probabilistic Models for Robot Trajectories, Ph.D. Thesis.
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Parisi, S. (2019). Reinforcement Learning with Sparse and Multiple Rewards, PhD Thesis.
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Koc, O. (2018). Optimal Trajectory Generation and Learning Control for Robot Table Tennis, PhD Thesis.
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Lioutikov, R. (2018). Parsing Motion and Composing Behavior for Semi-Autonomous Manipulation, PhD Thesis.
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Veiga, F. (2018). Toward Dextrous In-Hand Manipulation through Tactile Sensing, PhD Thesis.
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Manschitz, S. (2017). Learning Sequential Skills for Robot Manipulation Tasks, PhD Thesis.
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Paraschos, A. (2017). Robot Skill Representation, Learning and Control with Probabilistic Movement Primitives, PhD Thesis.
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Vinogradska, J. (2017). Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation, PhD Thesis.
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Calandra, R. (2016). Bayesian Modeling for Optimization and Control in Robotics, PhD Thesis.
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Daniel, C. (2016). Learning Hierarchical Policies from Human Feedback, PhD Thesis.
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Hoof, H.v. (2016). Machine Learning through Exploration for Perception-Driven Robotics, PhD Thesis.
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Kroemer, O. (2015). Machine Learning for Robot Grasping and Manipulation, PhD Thesis.
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Muelling, K. (2013). Modeling and Learning of Complex Motor Tasks: A Case Study with Robot Table Tennis, PhD Thesis.
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Wang, Z. (2013). Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Model, PhD Thesis.
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Kober, J. (2012). Learning Motor Skills: From Algorithms to Robot Experiments, PhD Thesis.
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Nguyen-Tuong, D (2011). Model Learning in Robot Control, PhD Thesis (Completed at IAS/Tuebingen before move to TU Darmstadt).
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Completed Master Theses
Hansel, K. (2021). Probabilistic Dynamic Mode Primitives, Master Thesis.
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Moos, J. (2021). Approximate Variational Inference for Mixture Models, Master Thesis.
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Patzwahl, A. (2021). Multi-sensor Fusion for Target Motion Prediction with an Application to Robot Baseball ||, Master Thesis.
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Rathjens, J. (2021). Accelerated Policy Search, Master Thesis.
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Borhade, P. (2020). Multi-agent reinforcement learning for autonomous driving, Master Thesis.
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Dorau, T. (2020). Distributionally Robust Optimization for Optimal Control, Master Thesis.
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Galljamov, R. (2020). Sample-Efficient Learning-Based Controller for Bipedal Walking in Robotic Systems, Master Thesis.
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Georgos, A. (2020). Robotics under Partial Observability, Master Thesis.
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Lautenschlaeger, T. (2020). Variational Inference for Switching Dynamics, Master Thesis.
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Mentzendorff, E.; (2020). Multi-Objective Deep Reinforcement Learning through Manifold Optimization, Master Thesis.
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Ploeger, K. (2020). High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards.
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Schotschneider, A. (2020). Learning High-Level Behavior for Autonomous Vehicles, Master Thesis.
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Semmler, M. (2020). Sequential Bayesian Optimal Experimental Design for Nonlinear Dynamics, Master Thesis.
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Sharma, S. (2020). SAC-RL: Continuous Control of Wheeled Mobile Robot for Navigation in a Dynamic Environment, Master Thesis.
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Tekam, S. (2020). A Gaussian Mixture ModelApproach to Off-Policy PolicyGradient Estimation, Master Thesis.
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Tengang, V. M. (2020). 3D Pose Estimation for Robot Mikado, Master Thesis.
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Weimar, J. (2020). Exploring Intrinsic Motivation for Quanser Reinforcement Learning Benchmark Systems, Master Thesis.
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Williamson, L. (2020). Learning Nonlinear Dynamical Systems with the Koopman Operator, Master Thesis.
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Zecevic, M. (2020). Learning Algorithms, Invariances, and the Real World, Master Thesis.
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Baig, I. (2019). Deep End-to-End Calibration Thesis, Master Thesis.
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Becker, P. (2019). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation, Master Thesis.
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Bous, F. (2019). Generating Spectral Envelopes for Singing Synthesis with Neural Networks, Master Thesis.
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Carvalho, J.A.C. (2019). Nonparametric Off-Policy Policy Gradient, Master Thesis.
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Cui, K. (2019). A Study on TD-Regularized Actor-Critic Methods, Master Thesis.
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Delfosse, Q. (2019). Grasping Objects Using a Goal-Discovering Architecture for Intrinsically-Motivated Learning, Master Thesis.
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Eschenbach, M. (2019). Metric-based Imitation Learning, Master Thesis.
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Hartmann, V. (2019). Efficient Exploration using Value Bounds in Deep Reinforcement Learning, Master Thesis.
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Hoffmann, D. (2019). Pedestrian Detection, Tracking and Intention Prediction in the context of autonomous Driving, Master Thesis.
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Hubecker, S. (2019). Curiosity Driven Reinforcement Learning for Autonomous Driving, Master Thesis.
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Huegelmann, N. (2019). Generating adaptable and reusable robot movements for a robot kitchen, Master Thesis.
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Jukonyte, L. (2019). Probabilistic Motion and Intention Prediction for Autonomous Vehicles, Master Thesis.
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Kircher, M. (2019). Learning from Human Feedback: a Comparison of Interactive Reinforcement Learning Algorithms, Master Thesis.
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Klink, P. (2019). Generalization and Transferability in Reinforcement Learning, Master Thesis.
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Knaust, M. (2019). Intuitive imitation learning for one-handed and bimanual tasks using ProMPs, Master Thesis.
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Laux, M. (2019). Deep Adversarialreinforcement Learning for Object Disentangling, Master Thesis.
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Liu, Z. (2019). Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm, Master Thesis.
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Mohan, D.S. (2019). Learning hand adjustments for haptic interactions, Master Thesis.
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Nass, D. (2019). Risk-Sensitive Policy Search with Applications to Robot-Badminton, Master Thesis.
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Nickl, P. (2019). Bayesian Inference for Regression Models using Nonparametric Infinite Mixtures, Master Thesis.
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Pal, S. (2019). Deep Robot Reinforcement Learning for Assisting a Human, Master Thesis.
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Sadybakasov, A. (2019). Learning Vision-Based Tactile Skills for Robotic Architectural Assembly, Master Thesis.
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Saoud, H. (2019). Improving Sample-Efficiency with a Model-Based Deterministic Policy Gradient, Master Thesis.
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Schultheis, M. (2019). Approximate Bayesian Reinforcement Learning for System Identification, Master Thesis.
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Weigand, S. (2019). Guided Reinforcement Learning Under Partial Observability, Master Thesis.
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Wilberg, A. (2019). An Exploration of Interacting Machine Learning Methods for Agent Navigation, Master Thesis.
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Woelker, A. (2019). Local Pixel Manipulation Detection with Deep Neural Networks, Master Thesis.
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Zhang, S. (2019). Integration of self-imitation and model-based learning to actor-critic algorithms, Master Thesis.
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Ziese, A. (2019). Fast Multi-Objective Redundancy Resolution for Highly-Redundant Mobile Robots, Master Thesis.
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Brandherm, F. (2018). Learning Replanning Policies with Direct Policy Search, Master Thesis.
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Celik, O. (2018). Chance Constraints for Stochastic Optimal Control and Stochastic Optimization, Master Thesis.
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Dienlin, D. (2018). Generative Adverserial Models for Deep Driving, Master Thesis.
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Dittmar, D. (2018). Distributed Reinforcement Learning with Neural Networks for Robotics, Master Thesis.
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Ritter, C. (2018). Deep Learning of Inverse Dynamic Models, Master Thesis.
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Song, Y. (2018). Minimax and entropic proximal policy optimization, Master Thesis.
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Thai, H. L. (2018). Deep Reinforcement Learning for POMDPs, Master Thesis.
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Trick, S. (2018). Multimodal Uncertainty Reduction for Intention Recognition in a Human-Robot Environment, Master Thesis.
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Wang, Z. (2018). Representation Learning for Tactile Manipulation, Master Thesis.
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Zhi, R. (2018). Deep reinforcement learning under uncertainty for autonomous driving, Master Thesis.
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Ahmad, P. (2017). Analysis of Financial Big Data using Machine Learning and Graph Processing, Master Thesis.
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Beckmann, L. (2017). Lane estimation with deep neural networks, Master Thesis.
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Gabriel, A. (2017). Empowered Skills, Master Thesis.
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Gadiya, P. (2017). Large Scale Real-Time Data Analytics Platform for Energy Exchange, Master Thesis.
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Gondaliya, K. (2017). Learning to Categorize Issues in Distributed Bug Tracker Systems, Master Thesis.
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Hoelscher, J. (2017). Interactive Planning Under Uncertainty, Master Thesis.
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Pinsler, R. (2017). Data-Efficient Learning of Robotic Grasps From Human Preferences, Master Thesis.
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Sharma, D. (2017). Adaptive Training Strategies for Brain Computer Interfaces, Master Thesis.
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Shinde, S. (2017). POMDPs for Continuous States and Observations for Robotics, Master Thesis.
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Weibrecht, N. (2017). Auswertung von Sensordaten mit Machine Learning Algorithmen, Master Thesis.
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Abbenseth, J. (2016). Cooperative Path-Planning for Service Robots, Master Thesis.
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Abdulsamad, H. (2016). Stochastic Optimal Control with Linearized Dynamics, Master Thesis.
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Achieser, I. (2016). Potential evaluation of eye- and headtracking data as a robust and real-time capable predictor for driver intention detection and integration into an algorithm for maneuver prediction, Master Thesis.
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Belousov, B. (2016). Optimal Control of Ball Catching, Master Thesis.
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Hüttenrauch, M. (2016). Guided Deep Reinforcement Learning for Robot Swarms, Master Thesis.
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Hesse, T. (2016). Learning a Filter for Noise Attenuation in EEG Data for Brain-Computer Interfaces, Master Thesis.
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Koert, D. (2016). Combining Human Demonstrations and Motion Planning for Movement Primitive Optimization, Master Thesis.
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Kohlschuetter, J. (2016). Learning Probabilistic Classifiers from Electromyography Data for Predicting Knee Abnormalities, Master Thesis.
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Luck, K. (2016). Multi-Group Factor Extension of the GrouPS algorithm and Real-World Robot Learning, Master Thesis.
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Schuster, R. (2016). 3D Object Proposals from Stereo and Optical Flow, Master Thesis.
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Stapf, E. (2016). Predicting Traffic Flows for Traffic Engineering in Software-Defined Networks, Master Thesis.
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Stark, S. (2016). Learning Probabilistic Feedforward and Feedback Policies for Generating Stable Walking Behaviors, Master Thesis.
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Wilbers, D. (2016). Context-driven Movement Primitive Adaptation, Master Thesis, IAS, TU Darmstadt.
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Novoty, M. (2015). Application of Decision-Support Technologies for an Autonomous Evasion System for UAVs, Master Thesis.
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Tanneberg, D. (2015). Spiking Neural Networks Solve Robot Planning Problems, Master Thesis.
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Vandommele, T. (2015). Entwicklung eines Algorithmus zur Klassifikation von Schläfrigkeit durch videobasierte Fahrerbeobachtung, Master Thesis.
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Wieland, A. (2015). Probabilistic Methods for Forecasting of Electric Load Profiles, Master Thesis.
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Arenz, O. (2014). Feature Extraction for Inverse Reinforcement Learning, Master Thesis.
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Barnikol, S. (2014). Machine Learning for Active Gait Support with a Powered Ankle Prosthesis, Master Thesis.
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Chebotar, Y. (2014). Learning Robot Tactile Sensing for Object Manipulation, Master Thesis.
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Dann, C. (2014). Value-Function-Based Reinforcement Learning with Temporal Differences, Masters Thesis.
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Ewerton, M. (2014). Modeling Human-Robot Interaction with Probabilistic Movement Representations, Master Thesis.
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Gebhardt, G.H.W. (2014). Embedding Kalman Filters into Reproducing Kernel Hilbert Spaces, Master Thesis.
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Kamthe, S. (2014). Multi-modal Inference in Time Series, Master Thesis.
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Manschitz, S. (2014). Learning Sequential Skills for Robot Manipulation Tasks, Master Thesis.
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Merfels, C. (2014). Large-scale probabilistic feature mapping and tracking for autonomous driving, Masters Thesis.
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Mindt, M. (2014). Probabilistic Inference for Movement Planning in Humanoids, Master Thesis.
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Mundo, J. (2014). Extracting Low-Dimensional Control Variables for Movement Primitives, Master Thesis.
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Reubold, J. (2014). 3D Object Reconstruction from Partial Views, Master Thesis.
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Ringwald, J. (2014). Combination of Movement Primitives for Robotics, Master Thesis.
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Zeiss, S. (2014). Manipulation Skill for Robotic Assembly, Master Thesis.
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Englert, P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Master Thesis.
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Haji Ghasemi, N. (2013). Approximate Gaussian Process Inference with Periodic Kernels, Master Thesis.
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Lioutikov, R. (2013). Learning time-dependent feedback policies with model-based policy search, Master Thesis.
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Schmitt, F. (2013). Probabilistic Nonlinear Model Predictive Control based on Pontryagin`s Minimum Principle, Master Thesis.
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Daniel, C. (2012). Hierarchical Relative Entropy Policy Search, Masters Thesis.
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Gopalan, N. (2012). Feedback Error Learning for Gait Acquisition, Master Thesis.
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Zhou, R. (2012). Free Space Detection Based On Occupancy Gridmaps, Masters Thesis.
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Muelling, K. (2009). Modeling Human Table Tennis, MSc Thesis (Completed at IAS/Tuebingen before move to TU Darmstadt) .
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Kober, J. (2008). Reinforcement Learning for Motor Primitives, MSc Thesis (Completed at IAS/Tuebingen before move to TU Darmstadt) .
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Completed Bachelor Theses
Helfenstein, F. (2021). Benchmarking Deep Reinforcement Learning Algorithms, Bachelor Thesis.
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Zoeller, M. (2021). Graph Neural Networks forModel-Based ReinforcementLearning, Bachelor Thesis.
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Baierl, M. (2020). Learning Action Representations For Primitives-Based Motion Generation, Bachelor Thesis.
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Damken, F. (2020). Variational Autoencoders for Koopman Dynamical Systems, Bachelor Thesis.
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Eiermann, A. (2020). Optimierung von Biologischen Systemen, Bachelor Thesis.
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Kirschner, M. (2020). Integration of LIDAR SLAM for an automous vehicle, Bachelor Thesis.
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Nukovic, L. (2020). Evaluation of the Handshake Turing Test for anthropomorphic Robots, Bachelor Thesis.
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Scharf, F. (2020). Proximal Policy Optimization with Explicit Intrinsic Motivation, Bachelor Thesis.
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Stadtmueller, J. (2020). Dimensionality Reduction of Movement Primitives in Parameter Space, Bachelor Thesis.
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Divo, F. (2019). Trajectory Based Upper Body Gesture Recognition for an Assistive Robot, Bachelor Thesis.
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Ebeling, L. (2019). Experimental validation of an MPC-POMDP model of ball catching, Bachelor Thesis.
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Hensel, M. (2019). Correlated Exploration in Deep Reinforcement Learning, Bachelor Thesis.
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Kaiser, F. (2019). Towards a Robot Skill Library Using Hierarchy, Composition and Adaptation, Bachelor Thesis.
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Keller, L. (2019). Application of state-of-the-art RL algorithms to robotics simulators, Bachelor Thesis.
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Kinold, J. (2019). Development of a Simulation Model for an Autonomous Vehicle, Bachelor Thesis.
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Lang, M. (2019). Imitation Learning for Highlevel Robot Behavior in the Context of Elderly Assistance, Bachelor Thesis.
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Lutz, P. (2019). Automatic Segmentation and Labeling for Robot Table Tennis Time Series, Bachelor Thesis.
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Suess, J. (2019). Robust Control for Model Learning, Bachelor Thesis.
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Weiland, C. (2019). Deep Model-based Reinforcement Learning: Propagating Rewards Backwards through the Model for All Time-Steps, Bachelor Thesis.
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Borg, A. (2018). InfiniteMixture Policies in Reinforcement Learning, Bachelor Thesis.
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Khaled, N. (2018). Benchmarking Reinforcement Learning Algorithms on Tetherball Games, Bachelor Thesis.
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Kolev, Z. (2018). Joint Learning of Humans and Robots, Bachelor Thesis.
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Rinder, S. (2018). Trajectory Kernels for Bayesian Optimization, Bachelor Thesis.
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Schneider, T. (2018). Guided Policy Search for In-Hand Manipulation, Bachelor Thesis.
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Schotschneider, A. (2018). Collision Avoidance in Uncertain Environments for Autonomous Vehicles using POMDPs.
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Tschirner, J. (2018). Boosted Deep Q-Network, Bachelor Thesis.
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Fiebig, K.-H. (2017). Multi-Task Logistic Regression in Brain-Computer Interfaces, Bachelor Thesis.
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Frisch, Y. (2017). The Effects of Intrinsic Motivation Signals on Reinforcement Learning Strategies, Bachelor Thesis.
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Hesse, R. (2017). Development and Evaluation of 3D Autoencoders for Feature Extraction, Bachelor Thesis.
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Lolkes, C. (2017). Incremental Imitation Learning with Estimation of Uncertainty, Bachelor Thesis.
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Pfanschilling, V. (2017). Self-Programming Mutation and Crossover in Genetic Programming for Code Generation, Bachelor Thesis.
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Polat, H. (2017). Nonparametric deep neural networks for movement planning, Bachelor Thesis.
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Rother, D. (2017). Transferring Insights on Biological Sleep to Robot Motor Skill Learning.
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Semmler, M. (2017). Exploration in Deep Reinforcement Learning, Bachelor Thesis.
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Szelag, S. (2017). Transferring Insights on Mental Training to Robot Motor Skill Learning, Bachelor Thesis.
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Thiem, S. (2017). Simulation of the underactuated Sake Robotics Gripper in V-REP and ROS, Bachelor Thesis.
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Zecevic, M. (2017). Matching Bundles of Axons Using Feature Graphs.
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Plage, L. M. (2016). Reinforcement Learning for tactile-based finger gaiting, Bachelor Thesis.
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Alte, D. (2016). Control of a robotic arm using a low-cost BCI, Bachelor Thesis.
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Becker, P. (2016). Learning Deep Feature Spaces for Nonparametric Inference, Bachelor Thesis.
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Grossberger, L. (2016). Towards a low-cost cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis, Bachelor Thesis.
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Klink, P (2016). Model Learning for Probabilistic Movement Primitives, Bachelor Thesis.
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Marg, V. (2016). Reinforcement Learning for a Dexterous Manipulation Task, Bachelor Thesis.
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Nakatenus, M. (2016). Multi-Agent Reinforcement Learning Algorithms, Bachelor Thesis.
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Palenicek, D. (2016). Reinforcement Learning for Mobile Missile Launching, Bachelor Thesis.
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Ramstedt, S. (2016). Deep Reinforcement Learning with Continuous Actions, Bachelor Thesis.
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Schultheis, M. (2016). Learning Priors for Error-related Decoding in EEG data for Brain-Computer Interfacing, Bachelor Thesis.
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Unverzagt, F.T. (2016). Modeling Robustness for Multi-Objective Optimization, Bachelor Thesis.
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Alexev, S. (2015). Reinforcement Learning für eine mobile Raketenabschußplattform, Bachelor Thesis.
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Berninger K. (2015). Hierarchical Policy Search Algorithms, Bachelor Thesis.
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Blank, A. (2015). Learning a State Representation for a Game Agent’s Reactive Behaviour, Bachelor Thesis.
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End, F. (2015). Learning Versatile Solutions with Policy Search, Bachelor Thesis.
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Mayer C. (2015). Learning to Sequence Movement Primitives for Rhythmic Tasks, Bachelor Thesis.
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Schaefer, A. (2015). Prediction of Finger Flexion from ECoG Data with a Deep Neural Network, Bachelor Thesis.
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Amend, S. (2014). Feature Extraction for Policy Search, Bachelor Thesis.
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Brandl, S. (2014). Learning to Pour Using Warped Features, Bachelor Thesis.
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Hesse, T. (2014). Spectral Learning of Hidden Markov Models, Bachelor Thesis.
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Hochlaender, A. (2014). Deep Learning for Reinforcement Learning in Pacman, Bachelor Thesis.
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Hoelscher, J. (2014). Tactile Exploration of Object Properties, Bachelor Thesis.
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Huhnstock, N. (2014). Tactile Sensing for Manipulation, Bachelor Thesis.
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Laux, M. (2014). Online Feature Learning for Reinforcement Learning, Bachelor Thesis.
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Luck, K. (2014). Latent Space Reinforcement Learning, Bachelor Thesis.
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Mattmann, A. (2014). Modeling How To Catch Flying Objects: Optimality Vs. Heuristics, Bachelor Thesis.
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Schroecker, Y. (2014). Artificial Curiosity for Motor Skill Learning, Bachelor Thesis.
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Smyk, M. (2014). Learning Generalizable Models for Compliant Robots, Bachelor Thesis.
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Thai, H.L. (2014). Laplacian Mesh Editing for Interaction Learning, Bachelor Thesis.
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von Willig, J. (2014). Reinforcement Learning for Heros of Newerth, Bachelor Thesis.
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Notz, D. (2013). Reinforcement Learning for Planning in High-Dimensional Domains, Bachelor Thesis.
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Pfretzschner, B. (2013). Autonomous Car Driving using a Low-Cost On-Board Computer, Bachelor Thesis.
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Schoengen, S. (2013). Visual feature learning for interactive segmentation, Bachelor Thesis.
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Distler, M. (2012). Koennen Lernalgorithmen interagieren aehnlich wie im Gehirn?, Bachelor Thesis.
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Hensch, P. (2012). Comparing Reinforcement Learning Algorithms on Tic-Tac-Toe, Bachelor Thesis.
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Hess, S. (2012). Levitation Sphere, Bachelor Thesis.
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Sharma, D. (2012). Combining Reinforcement Learning and Feature Extraction, Bachelor Thesis.
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Zimpfer, A. (2012). Vergleich verschiedener Lernalgorithmen auf einem USB-Missilelauncher, Bachelor Thesis.
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Honors Theses and Advanced Design Projects
Smyk, M. (2016). Model-based Control and Planning on Real Robots, Honors Thesis.
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Koert, D. (2015). Inverse Kinematics for Optimal Human-Robot Collaboration, Honors Thesis.
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Abdulsamad, H.; Buchholz, T.; Croon, T; El Khoury, M. (2014). Playing Tetherball with Compliant Robots, Advanced Design Project.
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Ho, D.; Kisner, V. (2014). Trajectory Tracking Controller for a 4-DoF Flexible Joint Robotic Arm, Advanced Design Project.
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Completed Seminar Theses
Alles, I (2012). Models for Biological Motor Control: Modules of Movements, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Arenz, O. (2012). Extensive Games, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Arenz, O. (2013). Inverse Optimal Control, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Dann, C. (2012). Algorithms for Fast Gradient Temporal Difference Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Dittmar, D. (2013). Slice Sampling, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Englert, P. (2012). Locally Weighted Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Fischer, A. (2012). Inverse Reinforcement Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Gabriel, A. (2012). An introduction to Structural Learning - A new approach in Reinforcement Learning, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Glaser, C. (2012). Learning in Reality: A case study of Stanley, the robot that Won the DARPA Challenge, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Goppalan, N. (2012). Gaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Graber, T. (2012). Models for Biological Motor Control: Optimality Principles, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Hardock, S. (2012). Applications in Robot Helicopter Acrobatics, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Isaak, J. (2012). Interaction Learning, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Kruk, S. (2013). Planning with Multiple Agents, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Kunz, F. (2013). An Introduction to Temporal Difference Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Kutschke, M. (2012). Imitation Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Lioutikov, R. (2012). Machine learning and the brain, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Mindt, M. (2012). Learning robot control, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Mogk, R. (2012). Efficient Planning under Uncertainty with Macro-actions, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Mueck, J. (2012). Learning physical Models of Robots, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Pignede, T. (2012). Evolution of Reinforcement Learning in Games or How to Win against Humans with Intelligent Agents, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Ploetz, T. (2012). Deterministic Approximation Methods in Bayesian Inference, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Reubold, J. (2012). Kernel Descriptors in comparison with Hierarchical Matching Pursuit, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Schnell, F. (2013). Hierarchical Reinforcement Learning in Robot Control, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Schoenberger, D. (2012). Planning in POMDPs, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Schroecker, Y. (2013). Planning for Relational Rules, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Stark, S. (2012). Do Reinforcement Learning Models Explain Neural Learning?, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Stein, A. (2013). Learning Robot Locomotion, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Swiezinski, L. (2013). Lifecycle of a Jeopardy Question Answered by Watson DeepQA, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Thiel, T. (2012). Learning in Robot Soccer, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Tschirsich, M. (2013). Learning Robot Control, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Viering, M. (2012). Hierarchical Reinforcement Learning in Robot Control, Seminar Thesis, Proceedings of the Robot Learning Seminar.
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Will, K. (2013). Autonomous Chess-Playing, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Zoellner, M. (2013). Reinforcement Learning in Games, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.
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Completed IP Projects
| Topic | Students | Adviser |
|---|---|---|
| Architectural Assembly: Simulation and Optimization | Jan Schneider | Boris Belousov, Georgia Chalvatzaki |
| Probabilistic Object Tracking using Depth Carmera | Jan Emrich, Simon Kiefhaber | Niklas Funk, Boris Belousov, Georgia Chalvatzaki |
| Bayesian Optimization for System Identification in Robot Air Hockey | Chen Xue. Verena Sieburger | Puze Liu, Davide Tateo |
| Benchmarking MPC Solvers in the Era of Deep Reinforcement Learning | Darya Nikitina, Tristan Schulz | Joe Watson |
