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: Theses are only for TU Darmstadt students who can directly contact Team Members for more information. For exceptional external students, we offer Theses as well, these need to contact Jan Peters by jan.peters@tu-darmstadt.de first.
Ongoing
Ongoing B.Sc. and M.Sc. Theses / B.Sc. und M.Sc. Themen in Bearbeitung
| Student | Advisor | Type | Topic |
|---|---|---|---|
| Moritz Schramm | Dorothea Koert | B.Sc. | Development of an Interactive Gesture Recognition System for an Assistive Robot |
| Max Niessing | Georgia Chalvatzaki | M.Sc. | Learning latent object representations for grasp generation with generative-adversarial models |
| Daljeet Nandha | Georgia Chalvatzaki | M.Sc. | Building Task Plans from Robot Knowledge Graphs |
| Daniel Musekamp | Oleg Arenz | M.Sc. | Amortized VIPS for Grasp Pose Sampling |
| Darya Nikitina | Joe Watson | B.Sc. | Inference Methods for Markov Decision Processes |
| Julian Kerl | Dorothea Koert | M.Sc. | Analysing Implicit Facial Behavior for Reinforcement Learning |
| Christian Weiland | Joao Carvalho | M.Sc. | Model Based Reinforcement Learning |
| Samuel Lokadjaja | Oleg Arenz | B.Sc. | Hierarchical Motherships for Space Exploration |
| Pascal Herrmann | Joao Carvalho | M.Sc. | RGB-D 6D Pose Estimation and Tracking for Ubongo3D |
| Niklas Kappes | Joao Carvalho | M.Sc. | Smooth Exploration |
| Jascha Hellwig | JoaoCarvalho, JulenUrainDeJesus | M.Sc. | Reinforcement Learning in Stable Vector Fields |
| Louis Sterker | VigneshPrasad | B.Sc. | Social Affordance Segmentation and Learning using Interaction Primitives |
| Oliver Järnefelt | Carlo D'Eramo | M.Sc. | Sparsely Collaborative Multi-Agent Reinforcement Learning |
| Erik Prescher | Vignesh Prasad | B.Sc. | SKID-HRI: Skill Discovery for Human-Robot Interaction |
| Jessica Löhr | Dorothea Koert | M.Sc. | Detection of Unexpected Human Behavior in Human-Robot Interaction in Shared Workspaces |
| Ruidi He | Tuan Dam | M.Sc. | AADD: Reinforcement Learning for Unbiased Autonomous Driving |
| Wenhua Bao | Junning Huang | M.Sc. | Model Based Hybrid Decision Making In Autonomous Driving |
| Johannes Weyel | Julen Urain De Jesus | M.Sc. | Inversing dynamics for Imitation Learning |
| Mark Baierl | Joao Carvalho, Julen Urain De Jesus | M.Sc. | Score-Based Imitation Learning |
| Sebastian Szelag | Niklas Funk, Georgia Chalvatzaki | M.Sc. | Multi-Agent Reinforcement Learning for Assembly |
| Théo Vincent | Carlo D'Eramo | M.Sc. | Projected Bellman Operator |
| Leon Keller | Joao Carvalho, Dorothea Koert | M.Sc. | Learning to Combine Local Controllers |
| Julien Brosseit | Joao Carvalho | M.Sc. | Policy Optimization with Learning and Planning in Continuous Spaces |
| Rukang Xu | SumanPal, VigneshPrasad | M.Sc. | SLAM-itation: SLAM-based robotic teleoperation |
| Paul-Otto Müller | Niklas Funk, Boris Belousov, Anton Savchenko | M.Sc. | Learning Interpretable Representations for Visuotactile Sensors |
| Xiangyu Xu | Niklas Funk, Boris Belousov | M.Sc. | Visuotactile Grasping From Human Demonstrations |
| Mohammad Atashak | Oleg Arenz | B.Sc. | Will it Blend? Learning to Coexecute Subskills |
| Benedikt Hahner | Joe Watson | M.Sc. | Sim2GP: Bayesian Dynamics Models via Differentiable Physics |
| Fabio Hilt | Joe Watson, Joao Carvalho | M.Sc. | Statistical Model Based Reinforcement Learning |
| Jonathan Kinzel | Pascal Klink | B.Sc. | Modelling and Control of a Spherical Pendulum on a 4-DOF Barret WAM |
Ongoing IP Projects
| Topic | Students | Adviser |
|---|---|---|
| 6D Pose Estimation and Tracking for Ubongo 3D | Marek Daniv | Joao Carvalho, Suman Pal |
| Task Taxonomy for robots in household | Amin Ali, Xiaolin Lin | Snehal Jauhri, Ali Younes |
| Stability analysis for control algorithms of Furuta Pendulum | Lu liu, Jiahui Shi, Yuheng Ouyang | Junning Huang, An Thai Le |
| Hybrid Motion-Force Planning on Manifolds | Chao Jin, Peng Yan, Liyuan Xiang | An Thai Le, Junning Huang |
| Task and Motion Planning for Sequential Assembly | Paul-Hallmann, Patrick Siebke, Nicolas Nonnengießer | Boris Belousov, Tim Schneider, Yuxi Liu |
| Learning the Gait for Legged Robot via Safe Reinforcement Learning | Joshua Johannson, Andreas Seidl Fernandez | Puze Liu, Davide Tateo |
| Active Perception for Mobile Manipulation | Sophie Lueth, Syrine Ben Abid, Amine Chouchane | Snehal Jauhri |
| Combining RL/IL with CPGs for Humanoid Locomotion | Henri Geiss | Firas Al-Hafez, Davide Tateo |
| Multimodal Attention for Natural Human-Robot Interaction | Aleksandar Tatalovic, Melanie Jehn, Dhruvin Vadgama, Tobias Gockel | Oleg Arenz, Lisa Scherf |
| Learn to play Tangram | Max Zimmermann, Dominik Marino, Maximilian Langer | Kay Hansel, Niklas Funk |
| Learning the Residual Dynamics using Extended Kalman Filter for Puck Tracking | Haoran Ding | Puze Liu, Davide Tateo |
| ROS Integration of Mitsubishi PA 10 robot | Jonas Günster | Puze Liu, Davide Tateo |
| Robot Juggling | Khan Nisar | Kai Ploeger |
Completed
Only TU Darmstadt Theses and the Theses of IAS members from before the move to TU Darmstadt are listed.
Completed PhD Theses
Abdulsamad, H. (2021). Statistical Machine Learning for Stochastic Structured Systems, Ph.D. Thesis.
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Arenz, O. (2021). Sample-Efficient I-Projections for Robot Learning, Ph.D. Thesis, TU Darmstadt.
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Loeckel, S. (2021). Machine Learning for Modeling and Analyzing of Race Car Drivers, Ph.D. Thesis.
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Lutter, M. (2021). Inductive Biases for Machine Learning in Robotics and Control, Ph.D. Thesis.
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Muratore, F. (2021). Randomizing Physics Simulations for Robot Learning, Ph.D. Thesis.
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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
Carrasco, H. (2022). Particle-Based Adaptive Sampling for Curriculum Learning, Master Thesis.
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Chue, X. (2022). Task Classification and Local Manipulation Controllers, Master Thesis.
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Kaiser, F. (2022). Multi-Object Pose Estimation for Robotic Applications in Cluttered Scenes, Master Thesis.
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Newswanger, A. (2022). Indoor Visual Navigation on Micro-Aerial Drones without External Infratructure, Master Thesis.
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Schneider, J. (2022). Model Predictive Policy Optimization amidst Inaccurate Models, Master Thesis.
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Sieburger, V. (2022). Development of a Baseline Agent in Robot Air Hockey, Master Thesis.
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Toelle, M. (2022). Curriculum Adversarial Reinforcement Learning, Master Thesis.
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Yang, Z. (2022). Exploring Gripping Behaviours and Haptic Emotions for Human-Robot Handshaking, Master Thesis.
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Zhang, K. (2022). Learning Geometric Constraints for Safe Robot Interactions, Master Thesis.
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Zhao, P. (2022). Improving Gradient Directions for Episodic Policy Search, Master Thesis.
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Brendgen, J. (2021). The Relation between Social Interaction And Intrinsic Motivation in Reinforcement Learning, Master Thesis.
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Buchholz, T. (2021). Variational Locally Projected Regression.
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Derstroff, C. (2021). Memory Representations for Partially Observable Reinforcement Learning, Master Thesis.
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Eich, Y. (2021). Distributionally Robust Optimization for Hybrid Systems, Master Thesis.
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Gruner, T. (2021). Wasserstein-Optimal Bayesian System Identification for Domain Randomization, Master Thesis.
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Hansel, K. (2021). Probabilistic Dynamic Mode Primitives, Master Thesis.
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He, J. (2021). Imitation Learning with Energy Based Model, Master Thesis.
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Huang, J. (2021). Multi-Objective Reactive Motion Planning in Mobile Manipulators, Master Thesis.
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Kaemmerer, M. (2021). Measure-Valued Derivatives for Machine Learning, Master Thesis.
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Lin, J.A. (2021). Functional Variational Inference in Bayesian Neural Networks, Master Thesis.
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Liu, L. (2021). Detection and Prediction of Human Gestures by Probabilistic Modelling, Master Thesis.
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Moos, J. (2021). Approximate Variational Inference for Mixture Models, Master Thesis.
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Palenicek, D. (2021). Dyna-Style Model-Based Reinforcement Learning with Value Expansion, 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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Schneider, T. (2021). Active Inference for Robotic Manipulation, Master Thesis.
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Sun, H. (2021). Can we improve time-series classification with Inverse Reinforcement Learning?, Master Thesis.
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Wang, Y. (2021). Bimanual Control and Learning with Composable Energy Policies, Master Thesis.
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Wegner, F. (2021). Learning Vision-Based Tactile Representations for Robotic Architectural Assembly, Master Thesis.
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Yang, H. (2021). Variational Inference for Curriculum Reinforcement Learning, Master Thesis.
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Ye, Z. (2021). Efficient Gradient-Based Variational Inference with GMMs, Master Thesis.
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Zhang, Y. (2021). Memory Representations for Partially Observable Reinforcement Learning, Master Thesis.
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Zhou, Z. (2021). Approximated Policy Search in Black-Box Optimization, 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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Klein, A. (2020). Learning Robot Grasping of Industrial Work Pieces using Dense Object Descriptors, Master Thesis.
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Krabbe, P. (2020). Learning Riemannian Movement Primitives for Manipulation, 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
Daniv, M. (2022). Graph-Based Model Predictive Visual Imitation Learning, Bachelor Thesis.
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Magnus, L. (2022). Real-time Object Tracking for Assembly, Bachelor Thesis.
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Menzenbach, S. (2022). Leveraging Learned Graph-based Heuristics for efficiently solving the Combinatorics of Assembly, Bachelor Thesis.
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Meser, M. (2022). Multi-Instance Pose Estimation for Robot Mikado, Bachelor Thesis.
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Siebenborn, M. (2022). Evaluating Decision Transformer Architecture on Robot Learning Tasks, Bachelor Thesis.
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Woortman, N. (2022). Comparing and Personalizing Human Following Behaviors for Mobile Ground Robots, Bachelor Thesis.
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Ali, M. (2021). An Educational Framework for Robot Learning, Bachelor Thesis.
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Gassen, M. (2021). Learning a library of Physical Interactions for Social Robots, Bachelor Thesis.
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Helfenstein, F. (2021). Benchmarking Deep Reinforcement Learning Algorithms, Bachelor Thesis.
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Schneider, L. (2021). Distributional Monte-Carlo Tree Search, 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). Infinite-Mixture 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
Sperling, J. (2021). Learning Robot Grasping of Industrial Work Pieces using Dense Object Descriptors, Honors Thesis.
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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 |
|---|---|---|
| Multi-sensorial reinforcement learning for robotic tasks | Rickmer Krohn | Georgia Chalvatzaki, Snehal Jauhri |
| Learning Behavior Trees from Video | Nick Dannenberg, Aljoscha Schmidt | Lisa Scherf, SumanPal |
| Subgoal-Oriented Shared Control | Zhiyuan Gao, Fengyun Shao | Kay Hansel |
| Task Taxonomy for robots in household | Amin Ali, Xiaolin Lin | Snehal Jauhri, Ali Younes |
| Learn to play Tangram | Maximilian Langer | Kay Hansel, Niklas Funk |
| Theory of Mind Models for HRI under partial Observability | Franziska Herbert, Tobias Niehues, Fabian Kalter | Dorothea Koert, Joni Pajarinen, David Rother |
| Learning Safe Human-Robot Interaction | Zhang Zhang | Puze Liu, Snehal Jauhri |
| Active-sampling for deep Multi-Task RL | Fabian Wahren | Carlo D'Eramo, Georgia Chalvatzaki |
| Interpretable Reinforcement Learning | patrick Vimr | Davide Tateo, Riad Akrour |
| Optimistic Actor Critic | Niklas Kappes,Pascal Herrmann | Joao Carvalho |
| Active Visual Search with POMDPs | Jascha Hellwig, Mark Baierl | Joao Carvalho, Julen Urain De Jesus |
| Utilizing 6D Pose-Estimation over ROS | Johannes Weyel | Julen Urain De Jesus |
| Learning Deep Heuristics for Robot Planning | Dominik Marino | Tianyu Ren |
| Learning Behavior Trees from Videos | Johannes Heeg, Aljoscha Schmidt and Adrian Worring | Suman Pal, Lisa Scherf |
| Learning Decisions by Imitating Human Control Commands | Jonas Günster, Manuel Senge | Junning Huang |
| Combining Self-Paced Learning and Intrinsic Motivation | Felix Kaiser, Moritz Meser, Louis Sterker | Pascal Klink |
| 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 | Johannes Weyel, 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 |
| Benchmarking advances in MCTS in Go and Chess | Lukas Schneider | Tuan Dam, Carlo D'Eramo |
| 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 |
| Enhancing Attention Aware Movement Primitives | Artur Kruk | Dorothea Koert |
| Towards Semantic Imitation Learning | Pengfei Zhao | Julen Urain & Georgia Chalvatzaki |
| Can we use Structured Inference Networks for Human Motion Prediction? | Hanyu Sun, Liu Lanmiao | Julen Urain & Georgia Chalvatzaki |
| Reinforcement Learning for Architectural Combinatorial Optimization | Jianpeng Chen, Yuxi Liu, Martin Knoll, Leon Wietschorke | Boris Belousov, Georgia Chalvatzaki, Bastian Wibranek |
| Architectural Assembly With Tactile Skills: Simulation and Optimization | Tim Schneider, Jan Schneider | Boris Belousov, Georgia Chalvatzaki, Bastian Wibranek |
| Bayesian Last Layer Networks | Jihao Andreas Lin | Joe Watson, Pascal Klink |
| BATBOT: BATter roBOT for Baseball | Yannick Lavan, Marcel Wessely | Carlo D'Eramo |
| Benchmarking Deep Reinforcement Learning | Benedikt Volker | Davide Tateo, Carlo D'Eramo, Tianyu Ren |
| Model Predictive Actor-Critic Reinforcement Learning of Robotic Tasks | Daljeet Nandha | Georgia Chalvatzaki |
| Dimensionality Reduction for Reinforcement Learning | Jonas Jäger | Michael Lutter |
| From exploration to control: learning object manipulation skills through novelty search and local adaptation | Leon Keller | Svenja Stark, Daniel Tanneberg |
| Robot Air-Hockey | Patrick Lutz | Puze Liu, Davide Tateo |
| Learning Robotic Grasp of Deformable Object | Mingye Zhu, Yanhua Zhang | Tianyu Ren |
| Teach a Robot to solve puzzles with intrinsic motivation | Ali Karpuzoglu | Georgia Chalvatzaki, Svenja Stark |
| Inductive Biases for Robot Learning | Rustam Galljamov | Boris Belousov, Michael Lutter |
| Accelerated Mirror Descent Policy Search | Maximilian Hensel | Boris Belousov, Tuan Dam |
| Foundations of Adversarial and Robust Learning | Janosch Moos, Kay Hansel | Svenja Stark, Hany Abdulsamad |
| Likelihood-free Inference for Reinforcement Learning | Maximilian Hensel, Kai Cui | Boris Belousov |
| Risk-Aware Reinforcement Learning | Maximillian Kircher, Angelo Campomaggiore, Simon Kohaut, Dario Perrone | Samuele Tosatto, Dorothea Koert |
| Video? | Jonas Eschmann, Robin Menzenbach, Christian Eilers | Boris Belousov, Fabio Muratore |
| Learning Symbolic Representations for Abstract High-Level Planning | Zhiyuan Hu, Claudia Lölkes, Haoyi Yang | Svenja Stark Daniel Tanneberg |
| Learning Perceptual ProMPs for Catching Balls | Axel Patzwahl | Dorothea Koert, Michael Lutter |
| Bayesian Inference for Switching Linear Dynamical Systems | Markus Semmler, Stefan Fabian | Hany Abdulsamad |
| Characterization of WAM Dynamics | Kai Ploeger | Dorothea Koert, Michael Lutter |
| Deep Reinforcement Learning for playing Starcraft II | Daniel Palenicek, Marcel Hussing, Simon Meister | Filipe Veiga |
| Enhancing Exploration in High-Dimensional Environments Δ | Lu Wan, Shuo Zhang | Simone Parisi |
| Building a Grasping Testbed | Devron Williams | Oleg Arenz |
| Online Dynamic Model Learning | Pascal Klink | Hany Abdulsamad, Alexandros Paraschos |
| Spatio-spectral Transfer Learning for Motor Performance Estimation | Karl-Heinz Fiebig | Daniel Tanneberg |
| From Robots to Cobots | Michael Burkhardt, Moritz Knaust, Susanne Trick | Dorothea Koert, Marco Ewerton |
| Learning Hand-Kinematics | Sumanth Venugopal, Deepak Singh Mohan | Gregor Gebhardt |
| Goal-directed reward generation | Alymbek Sadybakasov | Boris Belousov |
| Learning Grammars for Sequencing Movement Primitives | Kim Berninger, Sebastian Szelag | Rudolf Lioutikov |
| Learning Deep Feature Spaces for Nonparametric Inference | Philipp Becker | Gregor Gebhardt |
| Lazy skill learning for cleaning up a table | Lejla Nukovic, Moritz Fuchs | Svenja Stark |
| Reinforcement Learning for Gait Learning in Quadrupeds | Kai Ploeger, Zinan Liu | Svenja Stark |
| Learning Deep Feature Spaces for Nonparametric Inference | Philipp Becker | Gregor Gebhardt |
| Optimal Control for Biped Locomotion | Martin Seiler, Max Kreischer | Hany Abdulsamad |
| Semi-Autonomous Tele-Operation | Nick Heppert, Marius, Jeremy Tschirner | Oleg Arenz |
| Teaching People how to Write Japanese Characters | David Rother, Jakob Weimar, Lars Lotter | Marco Ewerton |
| Local Bayesian Optimization | Dmitry Sorokin | Riad Akrour |
| Bayesian Deep Reinforcement Learning -Tools and Methods- | Simon Ramstedt | Simone Parisi |
| Controlled Slip for Object Release | Steffen Kuchelmeister, Albert Schotschneider | Filipe Veiga |
| Learn intuitive physics from videos | Yunlong Song, Rong Zhi | Boris Belousov |
| Learn an Assembling Task with Swarm Robots | Kevin Daun, Marius Schnaubelt | Gregor Gebhardt |
| Learning To Sequence Movement Primitives | Christoph Mayer | Christian Daniel |
| Learning versatile solutions for Table Tennis | Felix End | Gerhard Neumann Riad Akrour |
| Learning to Control Kilo-Bots with a Flashlight | Alexander Hendrich, Daniel Kauth | Gregor Gebhardt |
| Playing Badminton with Robots | J. Tang, T. Staschewski, H. Gou | Boris Belousov |
| Juggling with Robots | Elvir Sabic, Alexander Wölker | Dorothea Koert |
| Learning and control for the bipedal walker FaBi | Manuel Bied, Felix Treede, Felix Pels | Roberto Calandra |
| Finding visual kernels | Fide Marten, Dominik Dienlin | Herke van Hoof |
| Feature Selection for Tetherball Robot Games | Xuelei Li, Jan Christoph Klie | Simone Parisi |
| Inverse Reinforcement Learning of Flocking Behaviour | Maximilian Maag, Robert Pinsler | Oleg Arenz |
| Control and Learning for a Bipedal Robot | Felix Treede, Phillip Konow, Manuel Bied | Roberto Calandra |
| Perceptual coupling with ProMPs | Johannes Geisler, Emmanuel Stapf | Alexandros Paraschos |
| Learning to balance with the iCub | Moritz Nakatenus, Jan Geukes | Roberto Calandra |
| Generalizing Models for a Compliant Robot | Mike Smyk | Herke van Hoof |
| Learning Minigolf with the BioRob | Florian Brandherm | Marco Ewerton |
| iCub Telecontrol | Lars Fritsche, Felix Unverzagt | Roberto Calandra |
| REPS for maneuvering in Robocup | Jannick Abbenseth, Nicolai Ommer | Christian Daniel |
| Learning Ball on a Beam on the KUKA lightweight arms | Bianca Loew, Daniel Wilberts | Christian Daniel |
| Sequencing of DMPs for Task- and Motion Planning | Markus Sigg, Fabian Faller | Rudolf Lioutikov |
| Tactile Exploration and Mapping | Thomas Arnreich, Janine Hoelscher | Tucker Hermans |
| Multiobjective Reinforcement Learning on Tetherball BioRob | Alexander Blank, Tobias Viernickel | Simone Parisi |
| Semi-supervised Active Grasp Learning | Simon Leischnig, Stefan Lüttgen | Oliver Kroemer |
