Former Students

IAS has had an amazing set of students and we are very proud of them. We list the whereabouts of our graduated M.Sc. students here as well as B.Sc. students who moved on to other great universities The B.Sc. students missing here stayed at TU Darmstadt and can be found at Abschlussarbeiten. We hope to see them again for their M.Sc. thesis and that they make this list.

• Christoph Dann: Accepted a Ph.D. position from the Carnegie Mellon University. His work with us can be found here:

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  Dann, C.; Neumann, G.; Peters, J. (2015). Policy Evaluation with Temporal Differences: A Survey and Comparison, Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling (ICAPS), pp.359-360.
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  Dann, C.; Neumann, G.; Peters, J. (2014). Policy Evaluation with Temporal Differences: A Survey and Comparison, Journal of Machine Learning Research (JMLR), 15, March, pp.809-883.
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  Dann, C. (2014). Value-Function-Based Reinforcement Learning with Temporal Differences, Masters Thesis.
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  Ackermann, M.; Dann, C.; Ploetz, T. (2013). Toward Successful Participation in Machine Learning Contests, Proceedings of Projektpraktikum Advanced Machine Learning.
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  Dann, C. (2012). Algorithms for Fast Gradient Temporal Difference Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.

• Yannick Schroecker: Accepted a Ph.D. position from the Georgia Institute of Technology. His work with us can be found here:

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  Schroecker, Y. (2014). Artificial Curiosity for Motor Skill Learning, Bachelor Thesis.
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  Schroecker, Y. (2013). Planning for Relational Rules, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.

• Yevgen Chebotar: Accepted a Ph.D. position from the University of Southern California.

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  Chebotar, Y. (2014). Learning Robot Tactile Sensing for Object Manipulation, Master Thesis.
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  Chebotar, Y.; Kroemer, O.; Peters, J. (2014). Learning Robot Tactile Sensing for Object Manipulation, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).

• Sanket Kamthe: Accepted a Ph.D. position from the University of Twente. His work with us can be found here:

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  Ben Amor, H.; Neumann, G.; Kamthe, S.; Kroemer, O.; Peters, J. (2014). Interaction Primitives for Human-Robot Cooperation Tasks , Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
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  Kamthe, S.; Peters, J.; Deisenroth, M. (2014). Multi-modal filtering for non-linear estimation, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
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  Kamthe, S. (2014). Multi-modal Inference in Time Series, Master Thesis.

• Simon Manschitz: Accepted a Ph.D. position from the TU Darmstadt in a joint project with Honda Research. His work with us can be found here:

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  Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2018). Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations, IEEE Robotics and Automation Letters (RA-L), 3, 2, pp.926-933.
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  Manschitz, S. (2017). Learning Sequential Skills for Robot Manipulation Tasks, PhD Thesis.
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  Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2016). Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
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  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
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  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations, Robotics and Autonomous Systems, 74, pp.97-107.
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  Manschitz, S. (2014). Learning Sequential Skills for Robot Manipulation Tasks, Master Thesis.
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  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Unscrew a Light Bulb from Demonstrations, Proceedings of ISR/ROBOTIK 2014.
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  Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Sequence Movement Primitives from Demonstrations, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).

• Jan Reubold: Accepted a Ph.D. position from the TU Dresden. His work with us can be found here:

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  Reubold, J. (2014). 3D Object Reconstruction from Partial Views, Master Thesis.
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  Reubold, J. (2012). Kernel Descriptors in comparison with Hierarchical Matching Pursuit, Seminar Thesis, Proceedings of the Robot Learning Seminar.

• Stefan Zeiss: His work with us can be found here:

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  Wahrburg, A.; Zeiss, S.; Matthias, B.; Peters, J.; Ding, H. (2015). Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).
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  Zeiss, S. (2014). Manipulation Skill for Robotic Assembly, Master Thesis.

• Christian Merfels: Accepted a Ph.D. position from the University of Bonn. His work with us can be found here:

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  Merfels, C. (2014). Large-scale probabilistic feature mapping and tracking for autonomous driving, Masters Thesis.

• Peter Englert: Accepted a Ph.D. position from the University of Stuttgart. His work with us can be found here:

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  Deisenroth, M.P.; Englert, P.; Peters, J.; Fox, D. (2014). Multi-Task Policy Search for Robotics, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
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  Englert, P.; Paraschos, A.; Peters, J.; Deisenroth, M. P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
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  Englert, P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Master Thesis.
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  Englert, P.; Paraschos, A.; Peters, J.;Deisenroth, M.P. (2013). Probabilistic Model-based Imitation Learning, Adaptive Behavior Journal, 21, pp.388-403.
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  Englert, P. (2012). Locally Weighted Learning, Seminar Thesis, Proceedings of the Autonomous Learning Systems Seminar.

• Rudolf Lioutikov: Accepted a Ph.D. position from the TU Darmstadt. His work with us can be found at this link.
  
• Nooshin Haji-Ghasemi: Her work with us can be found here:

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  Haji Ghassemi, N.; Deisenroth, M.P. (2014). Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
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  Berger, E.; Vogt, D.; Haji-Ghassemi, N.; Jung, B.; Ben Amor, H. (2013). Inferring Guidance Information in Cooperative Human-Robot Tasks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
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  Haji Ghasemi, N. (2013). Approximate Gaussian Process Inference with Periodic Kernels, Master Thesis.

• Felix Schmitt: Accepted a Ph.D. position within Bosch Research in collaboration with TU Chemnitz. His work with us can be found here:

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  Wagner, F.; Schmitt, F. (2013). Robot Beerpong: Model-Based Learning for Shifting Targets, Robot Beerpong: Model-Based Learning for Shifting Targets.
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  Schmitt, F. (2013). Probabilistic Nonlinear Model Predictive Control based on Pontryagin`s Minimum Principle, Master Thesis.

• Christian Daniel: Accepted a Ph.D. position from the TU Darmstadt. His work at IAS can be found at this link.
  
• Nakul Gopalan: Accepted a Ph.D. position from the Brown University.
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    Calandra, R.; Gopalan, N.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). Bayesian Gait Optimization for Bipedal Locomotion, Proceedings of the 2014 Learning and Intelligent Optimization Conference (LION8).
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    Gopalan, N.; Deisenroth, M. P.; Peters, J. (2013). Feedback Error Learning for Rhythmic Motor Primitives, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
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    Gopalan, N. (2012). Feedback Error Learning for Gait Acquisition, Master Thesis.
• Katharina Muelling: Accepted a Ph.D. position in the collaboration between the TU Darmstadt and the Max Planck Institute for Intelligent Systems. Successfully graduated with a Ph.D. and accepted a Post-Doc at the Carnegie Mellon University. Her work at IAS can be found at this link.
• Jens Kober: Accepted a Ph.D. position in the collaboration between the TU Darmstadt and the Max Planck Institute for Intelligent Systems. Successfully graduated with a Ph.D. which received the Best Robotics Ph.D. Thesis award, accepted a Post-Doc at Honda Research and has been offered a job as Assistant Professor at TU Delft. His work at IAS can be found at this link.

If we have accidentally omitted a successful graduate, please let Jan Peters know as soon as possible.