Bio:

Jan Peters graduated from the University of Hagen in 2000 with a Diplom-Informatiker (German M.Sc. in Computer Science) and from Munich University of Technology in 2001 with a Diplom-Ingenieur in Electrical Engineering (German M.Sc. in Electrical Engineering). In 2000-2001, he spent two semesters as visiting student at National University of Singapore. In 2002, he completed a M.Sc. in Computer Science and, in 2005, a M.S. in Mechanical Engineering both from USC. He joined USC's Computer Science Ph.D. program and the CLMC Lab in Fall 2001. Jan Peters has been a visiting research student at the Department of Robotics at the German Aerospace Research Center in Germany, at Siemens Advanced Engineering in Singapore and at the Department of Humanoid Robotics and Computational Neuroscience at the Advanded Telecommunication Research (ATR) Center in Japan.

Jan Peters graduated from University of Southern California with a Ph.D. in Computer Science in March 2007. He remains affiliated with the CLMC Lab as an adjunct researcher while starting a position at the Max-Planck Institute for Biological Cybernetics.

More information on Jan Peters can be found on his private homepage as well as his MPI website. The lab of Jan Peters can be found at MPI RoLL.

Research Interests:

Motor Control, Robotics, Machine learning

German Address:

MPI for Biological Cybernetics
Dept. Schölkopf
Spemannstraße 38
72076 Tübingen

USA Mailing Address:

University of Southern California
c/o Laura Lopez, Rm. 05
Hedco Neurosciences Building, HNB-05
3641 Watt Way
Los Angeles, CA 90089-2520, USA

Email:

Phone:

+49-7071-601-585

Fax:

+49-7071-601-552

Journal Papers

Peters, J.;Schaal, S. (in press). Reinforcement learning of motor skills with policy gradients, Neural Networks.
[Keywords: reinforcement learning, policy gradient methods, natural gradients, natural actor-critic, motor skills, motor primitives]
[Detail] [BibTeX] [PDF]

Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (in press). Operational space control: A theoretical and emprical comparison, International Journal of Robotics Research.
[Keywords: task space control, operational space control, redundancy resolution, humanoid robotics]
[Detail] [BibTeX] [PDF]

Peters, J., Schaal, S. (2008). Learning to Control in Operational Space, The International Journal of Robotics Research, 27, 2, pp.197-212.
[Keywords: operational space control, robot learning, reinforcement learning, reward-weighted regression]
[Detail] [BibTeX] [PDF]

Peters, J.; Schaal, S. (2008). Natural Actor-Critic, Neurocomputing, 71, 7-9, pp.1180-1190.
[Keywords: reinforcement learning, policy gradient, natural actor-critic, natural gradients]
[Detail] [BibTeX] [PDF]

Peters, J.;Mistry, M.;Udwadia, F. E.;Nakanishi, J.;Schaal, S. (2008). A unifying methodology for robot control with redundant DOFs, Autonomous Robots, 24, 1, pp.1-12.
[Detail] [BibTeX] [PDF]

Steinke, F, Hein, M., Peters, J., Schölkopf, B (2008). Manifold-valued Thin-Plate Splines with Applications in Computer Graphics, Computer Graphics Forum (Special Issue on Eurographics 2008), 27, 2.
[Detail] [BibTeX] [PDF]

Peters, J. (2007). Computational Intelligence: By Amit Konar, The Computer Journal, 50, 6, pp.758.
[Keywords: book review]
[Detail] [BibTeX]

Peters, J. (1998). Fuzzy Logic for Practical Applications, Kuenstliche Intelligenz (KI), 4, pp.60.
[Keywords: book review]
[Detail] [BibTeX]

Conference and Workshop Papers

Deisenroth, M.; Peters, J.; Rasmussen, C. (2008). Approximate Dynamic Programming with Gaussian Processes, American Control Conference.
[Detail] [BibTeX]

Nguyen-Tuong, D.; Peters, J.; Seeger, M.; Schoelkopf, B. (2008). Computed Torque Control with Nonparametric Regressions Techniques, American Control Conference.
[Detail] [BibTeX]

Deisenroth, M.P., Rasmussen, C.E.; Peters, J (2008). Model-Based Reinforcement Learning with Continuous States and Actions, Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008).
[Detail] [BibTeX]

Nguyen-Tuong, D.; Peters, J.; Seeger, M.; Schoelkopf, B. (2008). Learning Inverse Dynamics: a Comparison, Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008).
[Detail] [BibTeX]

Peters, J., Nguyen, D.; (2008). Real-Time Learning of Resolved Velocity Control on a Mitsubishi PA-10, International Conference on Robotics and Automation (ICRA).
[Detail] [BibTeX]

Hachiya, H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2008). Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation, Proceedings of the Twenty-Third National Conference on Artificial Intelligence (AAAI 2008).
[Detail] [BibTeX]

Wierstra,D.; Schaul,T.; Peters, J.; Schmidhuber, J. (2008). Episodic Reinforcement Learning by Logistic Reward-Weighted Regression, Proceedings of the International Conference on Artificial Neural Networks (ICANN).
[Detail] [BibTeX]

Sehnke, F.; Osendorfer, C; Rueckstiess, T; Graves, A.; Peters, J.; Schmidhuber, J. (2008). Policy Gradients with Parameter-based Exploration for Control, Proceedings of the International Conference on Artificial Neural Networks (ICANN).
[Detail] [BibTeX]

Peters, J., Schaal, S. (2007). Policy Learning for Motor Skills, Proceedings of 14th International Conference on Neural Information Processing (ICONIP).
[Keywords: machine learning, reinforcement learning, robotics, motor primitives, policy gradients, natural actor-critic, reward-weighted regression]
[Detail] [BibTeX]

Wierstra, D.; Foerster, A.; Peters, J.; Schmidhuber, J. (2007). Solving Deep Memory POMDPs with Recurrent Policy Gradients, Proceedings of the International Conference on Artificial Neural Networks (ICANN).
[Keywords: policy gradients, reinforcement learning]
[Detail] [BibTeX] [PDF]

Peters, J.; Schaal, S.; Schoelkopf, B. (2007). Towards Machine Learning of Motor Skills, Proceedings of Autonome Mobile Systeme (AMS).
[Keywords: motor skill learning, robotics, natural actor-critic, reward-weighted regeression]
[Detail] [BibTeX] [PDF]

Theodorou, E; Peters, J; Schaal, S. (2007). Reinforcement Learning for Optimal Control of Arm Movements, Abstracts of the 37st Meeting of the Society of Neuroscience..
[Keywords: optimal control,reinforcement learning, arm movements]
[Detail] [BibTeX]

Nakanishi, J.;Mistry, M.;Peters, J.;Schaal, S. (2007). Experimental evaluation of task space position/orientation control towards compliant control for humanoid robots, IEEE International Conference on Intelligent Robotics Systems (IROS 2007).
[Keywords: operational space control, quaternion, task space control, resolved motion rate control, resolved acceleration, force control]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2007). Reinforcement learning for operational space control, International Conference on Robotics and Automation (ICRA2007), pp.2111-2116.
[Keywords: operational space control, reinforcement learning, weighted regression, em-algorithm]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2007). Using reward-weighted regression for reinforcement learning of task space control, Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning.
[Keywords: reinforcement learning, cart-pole, policy gradient methods]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2007). Applying the episodic natural actor-critic architecture to motor primitive learning, Proceedings of the 2007 European Symposium on Artificial Neural Networks (ESANN).
[Keywords: reinforcement learning, policy gradient methods, motor primitives, natural actor-critic]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2007). Reinforcement learning by reward-weighted regression for operational space control, Proceedings of the International Conference on Machine Learning (ICML2007).
[Keywords: reinforcement learning, operational space control, weighted regression]
[Detail] [BibTeX] [PDF]

Peters, J.;Theodorou, E.;Schaal, S. (2007). Policy gradient methods for machine learning, INFORMS Conference of the Applied Probability Society.
[Keywords: policy gradient methods, reinforcement learning, simulation-optimization]
[Detail] [BibTeX] [PDF]

Riedmiller, M.;Peters, J.;Schaal, S. (2007). Evaluation of policy gradient methods and variants on the cart-pole benchmark, Proceedings of the 2007 IEEE Internatinal Symposium on Approximate Dynamic Programming and Reinforcement Learning.
[Keywords: reinforcement learning, cart-pole, policy gradient methods]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S.;Schšlkopf, B. (2007). Towards machine learning for motor skills, FachgespŠche Atonome Mobile Systeme (AMS 2007), pp.138-144, Springer.
[Keywords: reinforcement learning, autonomous robotics]
[Detail] [BibTeX] [PDF]

Peters, J.; Schaal, S. (2006). Policy Gradient Methods for Robotics, Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS).
[Keywords: policy gradient methods, reinforcement learning, robotics]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2006). Learning operational space control, in: Burgard, W.;Sukhatme, G. S.;Schaal, S. (eds.), Robotics: Science and Systems (RSS 2006), Cambridge, MA: MIT Press.
[Keywords: operational space control redundancy forward models inverse models compliance reinforcement leanring locally weighted learning]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2006). Reinforcement Learning for Parameterized Motor Primitives, Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN 2006).
[Keywords: motor primitives, reinforcement learning]
[Detail] [BibTeX] [PDF]

Ting, J.;Mistry, M.;Nakanishi, J.;Peters, J.;Schaal, S. (2006). A Bayesian approach to nonlinear parameter identification for rigid body dynamics, in: Burgard, W.;Sukhatme, G. S.;Schaal, S. (eds.), Robotics: Science and Systems (RSS 2006), Cambridge, MA: MIT Press.
[Keywords: bayesian regression linear models dimensionality reduction input noise rigid body dynamics parameter identification]
[Detail] [BibTeX] [PDF]

Peters, J.;Schaal, S. (2006). Policy gradient methods for robotics, Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006).
[Keywords: policy gradient methods, reinforcement learning, robotics]
[Detail] [BibTeX] [PDF]

Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2005). Comparative experiments on task space control with redundancy resolution, IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp.3901-3908.
[Keywords: manipulator dynamics redundant manipulators space optimization dynamical decoupling humanoid robots inverse kinematics motor coordination redundancy resolution robot dynamics seven-degree-of-freedom anthropomorphic robot arm task space control dynamical d]
[Detail] [BibTeX] [PDF]

Peters, J.;Vijayakumar, S.;Schaal, S. (2005). Natural Actor-Critic, in: Gama, J.;Camacho, R.;Brazdil, P.;Jorge, A.;Torgo, L. (eds.), Proceedings of the 16th European Conference on Machine Learning (ECML 2005), 3720, pp.280-291, Springer.
[Keywords: reinforcement learning, policy gradients, natural gradients]
[Detail] [BibTeX] [PDF]

Peters, J.;Mistry, M.;Udwadia, F. E.;Schaal, S. (2005). A new methodology for robot control design, The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005).
[Keywords: robot control, nonlinear control, gauss principle]
[Detail] [BibTeX] [PDF]

Peters, J.;Mistry, M.;Udwadia, F. E.;Cory, R.;Nakanishi, J.;Schaal, S. (2005). A unifying framework for the control of robotics systems, IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp.1824-1831.
[Detail] [BibTeX] [PDF]

Schaal, S.;Peters, J.;Nakanishi, J.;Ijspeert, A. (2004). Learning Movement Primitives, International Symposium on Robotics Research (ISRR2003), Springer.
[Keywords: movement primitives, supervised learning, reinforcment learning, locomotion, phase resetting, learning from demonstration]
[Detail] [BibTeX] [PDF]

Peters, J., Schaal, S. (2004). Learning Motor Primitives with Reinforcement Learning, Proceedings of the 11th Joint Symposium on Neural Computation.
[Keywords: natural policy gradients, motor primitives, natural actor-critic]
[Detail] [BibTeX]

Mohajerian, P.;Peters, J.;Ijspeert, A.;Schaal, S. (2003). A unifying computational framework for optimization and dynamic systemsapproaches to motor control, Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003).
[Keywords: computational motor control, optimization, dynamic systems, formal modeling]
[Detail] [BibTeX] [PDF]

Peters, J.;Vijayakumar, S.;Schaal, S. (2003). Reinforcement learning for humanoid robotics, Humanoids2003, Third IEEE-RAS International Conference on Humanoid Robots.
[Keywords: reinforcement learning, policy gradients, movement primitives, behaviors, dynamic systems, humanoid robotics]
[Detail] [BibTeX] [PDF]

Peters, J.;Vijayakumar, S.;Schaal, S. (2003). Scaling reinforcement learning paradigms for motor learning, Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003).
[Keywords: reinforcement learning, neurodynamic programming, actorcritic methods, policy gradient methods, natural policy gradient]
[Detail] [BibTeX] [PDF]

Schaal, S.;Peters, J.;Nakanishi, J.;Ijspeert, A. (2003). Control, planning, learning, and imitation with dynamic movement primitives, Workshop on Bilateral Paradigms on Humans and Humanoids, IEEE International Conference on Intelligent Robots and Systems (IROS 2003).
[Keywords: movement primitives, supervised learning, reinforcment learning, locomotion, phase resetting, learning from demonstration]
[Detail] [BibTeX] [PDF]

Burdet, E., Tee, K.P., Chew, C.M., Peters, J., Bt, V.L. (2001). Hybrid IDM/Impedance Learning in Human Movements, First International Symposium on Measurement, Analysis and Modeling of Human Functions Proceedings.
[Keywords: human motor control]
[Detail] [BibTeX]

Peters, J; Riener, R (2000). A real-time model of the human knee for application in virtual orthopaedic trainer, Proceedings of the 10th International Conference on Biomedical Engineering Conference (ICBME).
[Keywords: biomechanics, human motor control]
[Detail] [BibTeX]

Theses

Peters, J. (2007). Machine Learning of Motor Skills for Robotics, Ph.D. Thesis, Department of Computer Science, University of Southern California.
[Keywords: machine learning, reinforcement learning, robotics, motor primitives, policy gradients, natural actor-critic, reward-weighted regression]
[Detail] [BibTeX]