Autonomous Systems Labs: Machine Learning for Intelligent Systems and Robotics

Upcoming Talks

DateTimeLocation
30.04.201510:00-11:00S202 E302
Dorothea Koert, Research Talk. Inverse Kinematics for Optimal Human-robot Collaboration
DateTimeLocation
30.04.201511:00-12:00S202 E302
Herke van Hoof, Research Talk: Robot learning from vision and tactile sensing
DateTimeLocation
5.05.201517:00-18:30S202 E302
Katharina Mülling, Research Talk: Autonomy Infused Teleoperation with Application to BCI Manipulation
Welcome to the Computational Learning for Autonomous Systems Group and the Intelligent Autonomous Systems Group of the Computer Science Department of the Technische Universitaet Darmstadt.

Our research centers around the goal of bringing advanced motor skills to robotics using techniques from machine learning and control. Please check out our research or contact any of our lab members. As we originated out of the RObot Learning Lab in the Department for Empirical Inference and Machine Learning at the Max-Planck Institute of Intelligent Systems, we also have a few members in Tuebingen. We also collaborate with some of the excellent other autonomous systems groups at TU Darmstadt such as the Simulation, Systems Optimization and Robotics Group and the Locomotion Laboratory.

Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. While this aim has been a long-standing vision of artificial intelligence and the cognitive sciences, we have yet to achieve the first step of creating robots that can learn to accomplish many different tasks triggered by environmental context or higher-level instruction. The goal of our robot learning laboratory is the realization of a general approach to motor skill learning, to get closer towards human-like performance in robotics. We focus on the solution of fundamental problems in robotics while developing machine-learning methods.

Computational Learning for Autonomous Systems (CLAS)

The new group on Computational Learning for Autonomous Systems Group is headed by Gerhard Neumann, who is Assistant Professor at the TU-Darmstadt since September 2014. The main focus of the CLAS group is to investigate computational learning algorithms that allow artificial agents to autonomously learn new skills from interaction with the environment, humans or other agents. We believe that such autonomously learning agents will have a great impact in many areas of everyday life, for example, autonomous robots for helping in the household, care of the elderly or the disposal of dangerous goods.

An autonomously learning agent has to acquire a rich set of different behaviours to achieve a variety of goals. The agent has to learn autonomously how to explore its environment and determine which are the important features that need to be considered for making a decision. It has to identify relevant behaviours and needs to determine when to learn new behaviours. Furthermore, it needs to learn what are relevant goals and how to re-use behaviours in order to achieve new goals. In order to achieve these objectives, our research concentrates on hierarchical learning and structured learning of robot control policies, information-theoretic methods for policy search, imitation learning and autonomous exploration, learning forward models for long-term predictions, autonomous cooperative systems and multi-agent systems and the biological aspects of autonomous learning systems.

Intelligent Autonomous Systems (IAS)

In the Intelligent Autonomous Systems Group headed by Jan Peters since July 2014 at TU Darmstadt and since May 2007 at the Max Planck Institute, we develop methods for learning models and control policy in real time, see e.g., learning models for control and learning operational space control. We are particularly interested in reinforcement learning where we try push the state-of-the-art further on and received a tremendous support by the RL community. Much of our research relies upon learning motor primitives that can be used to learn both elementary tasks as well as complex applications such as grasping or sports.

Some more information on us fore the general public can be found in a long article in the Max Planck Research magazine, small stubs in New Scientist, WIRED and the Spiegel, as well as on the IEEE Blog on Robotics and Engadget.

Directions and Open Positions

In case that you are searching for our address or for directions on how to get to our lab, look at our contact information. We always have thesis opportunities for enthusiastic and driven Masters/Bachelors students (please contact Jan Peters or Gerhard Neumann). Check out the currently offered theses (Abschlussarbeiten) or suggest one yourself, drop us a line by email or simply drop by! We also occasionally have open Ph.D. or Post-Doc positions, see OpenPositions.

News

  1. Calandra, R.; Ivaldi, S.; Deisenroth, M.;Rueckert, E.; Peters, J. (2015). Learning Inverse Dynamics Models with Contacts, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Kroemer, O.; Daniel, C.; Neumann, G; van Hoof, H.; Peters, J. (2015). Towards Learning Hierarchical Skills for Multi-Phase Manipulation Tasks, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G. (2015). Extracting Low-Dimensional Control Variables for Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  1. Brandl, S.; Kroemer, O.; Peters, J. (2014). Generalizing Pouring Actions Between Objects using Warped Parameters, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Colome, A.; Neumann, G.; Peters, J.; Torras, C. (2014). Dimensionality Reduction for Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Ivaldi, S.; Peters, J.; Padois, V.; Nori, F. (2014). Tools for simulating humanoid robot dynamics: a survey based on user feedback, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  5. Rueckert, E.; Mindt, M.; Peters, J.; Neumann, G. (2014). Robust Policy Updates for Stochastic Optimal Control, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  1. Wang, Z.; Boularias, A.; Muelling, K.; Schoelkopf, B.; Peters, J. (accepted). Anticipatory Action Selection for Human-Robot Table Tennis, Artificial Intelligence.   See Details [Details]   BibTeX Reference [BibTex]
  2. Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Ai Poh, L.; Vadakkepat, V.; Neumann, G. (accepted). Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills, Artificial Intelligence.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Dann, C.; Neumann, G.; Peters, J. (2014). Policy Evaluation with Temporal Differences: A Survey and Comparison, Journal of Machine Learning Research, 15, March, pp.809-883.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Meyer, T.; Peters, J.; Zander, T.O.; Schoelkopf, B.; Grosse-Wentrup, M. (2014). Predicting Motor Learning Performance from Electroencephalographic Data, Journal of Neuroengineering and Rehabilitation, 11, 1.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  5. Muelling, K.; Boularias, A.; Mohler, B.; Schoelkopf, B.; Peters, J. (2014). Learning Strategies in Table Tennis using Inverse Reinforcement Learning, Biological Cybernetics, 108, 5, pp.603-619.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  6. Neumann, G.; Daniel, C.; Paraschos, A.; Kupcsik, A.; Peters, J. (2014). Learning Modular Policies for Robotics, Frontiers in Computational Neuroscience.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  7. Wierstra. D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; Schmidhuber, J. (2014). Natural Evolution Strategies, Journal of Machine Learning Research, 15, March, pp.949-980.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  8. Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Generalizing Movements with Information Theoretic Stochastic Optimal Control, Journal of Aerospace Information Systems, 11, 9, pp.579-595.   See Details [Details]   BibTeX Reference [BibTex]

Past News

  

zum Seitenanfang