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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.

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Record Number3236
Reference TypeJournal Article
Author(s)Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S.
Year2008
TitleOperational space control: A theoretical and emprical comparison
Journal/Conference/Book TitleInternational Journal of Robotics Research
Keywordstask space control, operational space control, redundancy resolution, humanoid robotics
AbstractDexterous manipulation with a highly redundant movement system is one of the hallmarks of hu- man motor skills. From numerous behavioral studies, there is strong evidence that humans employ compliant task space control, i.e., they focus control only on task variables while keeping redundant degrees-of-freedom as compliant as possible. This strategy is robust towards unknown disturbances and simultaneously safe for the operator and the environment. The theory of operational space con- trol in robotics aims to achieve similar performance properties. However, despite various compelling theoretical lines of research, advanced operational space control is hardly found in actual robotics imple- mentations, in particular new kinds of robots like humanoids and service robots, which would strongly profit from compliant dexterous manipulation. To analyze the pros and cons of different approaches to operational space control, this paper focuses on a theoretical and empirical evaluation of different methods that have been suggested in the literature, but also some new variants of operational space controllers. We address formulations at the velocity, acceleration and force levels. First, we formulate all controllers in a common notational framework, including quaternion-based orientation control, and discuss some of their theoretical properties. Second, we present experimental comparisons of these approaches on a seven-degree-of-freedom anthropomorphic robot arm with several benchmark tasks. As an aside, we also introduce a novel parameter estimation algorithm for rigid body dynamics, which ensures physical consistency, as this issue was crucial for our successful robot implementations. Our extensive empirical results demonstrate that one of the simplified acceleration-based approaches can be advantageous in terms of task performance, ease of parameter tuning, and general robustness and compliance in face of inevitable modeling errors.
Notesclmc
Volume27
Number6
Pages737-757
Short TitleOperational space control: A theoretical and emprical comparison
URL(s) http://www-clmc.usc.edu/publications/N/nakanishi-IJRR2008.pdf


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