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Welcome
Welcome to my homepage! My name is Jan Peters. My research centers around the goal of bringing advanced motor skills to robotics using techniques from machine learning and optimal control. You can check out my research interests and my publications for further information.
I have joined the Max-Planck Institute of Biological Cybernetics in 2007 as a Research Scientist and as Robot Learning Group Leader in the Department of Bernhard Schoelkopf. Before doing so, I completed a Ph.D. at the Department of Computer Science at the University of Southern California in sunny Los Angeles. There, I have been working with Stefan Schaal, Sethu Vijayakumar (now at U. Edinburgh, UK), and Firdaus Udwadia (Department of Mechanical Engineering). Chris Atkeson (Robotics Institute at CMU) and Gaurav Sukhatme also guided me to my thesis. Even longer ago, before joining USC, I studied computer science (with a focus on artificial intelligence), and electrical engineering (majoring in automation & control) in Germany and Singapore, worked in Germany, Japan, and Singapore. Furthermore, I obtained a Dipl.-Ing. (German MSEE) from Munich University of Technology and a Dipl.-Inform. (German MSCS) from Hagen University. At University of Southern California, I have obtained yet another Masters in Computer Science and, more recently, completed a Masters in Mechanical Engineering (Major: Dynamics & Nonlinear Control). Check out my curriculum vitae for more information.
At the Max-Planck Institute of Biological Cybernetics I have build up the new RObot Learning Lab (RoLL) working with four terrific robot learning students: Duy Nguyen-Tuong, Jens Kober, Katharina Muelling and Oliver Kroemer. We also had a couple of excellent research interns/external students collaborating with us: Gerhard Neumann (TU Graz), Hirotaka Hachiya (Tokyo Tech) and Marc Deisenroth (Cambridge Univ.).
As my research lies at the intersection between two fields, i.e., machine learning and robotics, I am always keen to bring members of both fields together. To do so, I have organized two NIPS workshops (Towards a New Reinforcement Learning! and Robotics Challenges for Machine Learning), one R:SS Workshop ( Learning for Locomotion), two IROS workshops (From motor to interaction learning in robots and Robotics Challenges for Machine Learning II), one ICRA workshop (Approaches to Sensorimotor Learning on Humanoid Robots) and one ECAI workshop (The 6th International Cognitive Robotics Workshop). My Co-Organizers included Pieter Abeel (U. Berkeley), Drew Bagnell (CMU), Dana Kulic (U. Waterloo), Jun Morimoto (ATR), Nick Roy (MIT), Stefan Schaal (USC), Olivier Sigaud (U.Paris 6), Russ Tedrake (MIT), Marc Toussaint (TU Berlin), Sethu Vijayakumar (U.Edingburgh), Gerhard Lakemeyer (RWTH Aachen, Germany), Yves Lespérance (York University, Canada), Fiora Pirri (University of Rome "La Sapienza", Italy), Ales Ude (Josef Stefan Institute, Slovenia), Tamim Asfour (U.Karlsruhe).
In 2008, Nick Roy (MIT), Russ Tedrake (MIT), Jun Morimoto (ATR) and I founded the IEEE Technical Committee on Robot Learning.
In 2009, Andrew Y. Ng (Stanford) and I have edited a Special Issue on Robot Learning in the Autonomous Robots journal. We have received 46 submissions and only accepted the best 8 papers. It required altogether 180 reviews written by roughly 100 colleagues to achieve this excellent selection.
In case that you are searching for my address or for directions on how to get to our lab and my office, look at my contact information. Alternatively, you can visit my official website.
Upcoming Events
- Plenary talk at LEMIR 2009 (Learning and Mining for Robotics workshop at ECML-PKDD).
- Dagstuhl Seminar: Cognition, Control and Learning for Robot Manipulation in Human Environments with Michael Beetz (TU München), Oliver Brock (U.Mass. in Amherst/TU Berlin), Gordon Cheng (ATR) from 16.08.09 to 21.08.09.
- Lecture at the RLSS Robot Learning Summer School at IST Lisbon.
- Invited Speaker at ISRR in Luzern in September 2009.
Recent News
- Invited talks at R:SS 2009 Workshops Regression in Robotics - Approaches and Applications and Creative Manipulation: Examples using the WAM.
- R:SS 2009 Workshops Bridging the gap between high-level discrete representations and low-level continuous behaviors with Dana Kulic, (U.Tokyo) and Pieter Abbel (U.California in Berkeley).
- Invited plenary lecture at 4th XVR Workshop & Joint PRESENCCIA and SKILLS PhD Symposium.
- ICRA 2009 Workshop: Approaches to Sensorimotor Learning on Humanoid Robots with Ales Ude, Tamim Asfour, Jun Morimoto and Stefan Schaal on 17.05.2009.
- Invited plenary lecture at Premières Journées Annuelles du GDR Robotique 2008 (French National Conference on Robotics).
- Duy Nguyen-Tuong's and my paper was finalist for the IROS 2008 Best Paper Award.
- IROS 2008 Workshop: From motor to interaction learning in robots with Olivier Sigaud (U.Paris 6) and Sethu Vijayakumar (U.Edingburgh).
- IROS 2008 Workshop: Robotics Challenges for Machine Learning II with Russ Tedrake (MIT), Nick Roy (MIT), Jun Morimoto (ATR).
- Invited talk on Motor Skill Learning for Cognitive Robotics at The 6th International Cognitive Robotics Workshop at ECAI 2008.
- Invited plenary lecture/Keynote Reinforcement Learning for Robotics at the European Workshop on Reinforcement Learning (EWRL).
- Keynote Unifying Imitation and Reinforcement Learning for Robotics at the Robotics: Science & Systems (R:SS), Workshop on Interactive Robotic Learning.
Current Publications
| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning |
| Abstract | One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importance for robotics and well-understood from an analytical point of view, it can be prohibitively hard to achieve accurate control in face of modeling errors, which are inevitable in com- plex robots, e.g., humanoid robots. In this paper, we suggest a learning approach for opertional space control as a direct inverse model learning problem. A ï¬rst important insight for this paper is that a physically cor- rect solution to the inverse problem with redundant degrees-of-freedom does exist when learning of the inverse map is performed in a suitable piecewise linear way. The second crucial component for our work is based on the insight that many operational space controllers can be understood in terms of a constrained optimal control problem. The cost function as- sociated with this optimal control problem allows us to formulate a learn- ing algorithm that automatically synthesizes a globally consistent desired resolution of redundancy while learning the operational space controller. From the machine learning point of view, this learning problem corre- sponds to a reinforcement learning problem that maximizes an immediate reward. We employ an expectation-maximization policy search algorithm in order to solve this problem. Evaluations on a three degrees of freedom robot arm are used to illustrate the suggested approach. The applica- tion to a physically realistic simulator of the anthropomorphic SARCOS Master arm demonstrates feasibility for complex high degree-of-freedom robots. We also show that the proposed method works in the setting of learning resolved motion rate control on real, physical Mitsubishi PA-10 medical robotics arm. |
| Notes | clmc |
| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |