Member: PubJanPeters

Peters, J.; Tedrake, R.; Roy, N.; Morimoto, J. (in press). Robot Learning, Encyclopedia of Machine Learning. [PDF]

Sigaud, O.; Peters, J. (in press). Robot Learning, Encyclopedia of the Sciences of Learning, Springer Verlag, Seel, Norbert M.. [PDF]

Peters, J.; Bagnell, J.A. (in press). Policy gradient methods, Encyclopedia of Machine Learning (invited article). [PDF]

Nguyen-Tuong, D.; Seeger, M.; Peters, J. (2010). Real-Time Local GP Model Learning, From Motor Learning to Interaction Learning in Robots, Springer Verlag, 264. [PDF]

Sigaud, O.; Peters, J. (2010). From Motor Learning to Interaction Learning in Robots, Studies in Computational Intelligence, Springer Verlag, 264. [PDF]

Kober,J.; Mohler, B.; Peters, J. (2010). Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling, From Motor Learning to Interaction Learning in Robots, Springer Verlag. [PDF]

Detry,R.; Baseski, E.; Popovic, M.; Touati, Y.; Krueger, N.; Kroemer, O.; Peters, J.; Piater, J. (2010). Learning Continuous Grasp Affordances by Sensorimotor Exploration, From Motor Learning to Interaction Learning in Robots, Springer Verlag, 264. [PDF]

Lesperance, Y.; Lakemeyer, G.; Peters, J.; Pirri, F. (2008). Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), July 21-22, 2008, Patras, Greece, ISBN 978-960-6843-09-9.

Peters, J. (2008). Machine Learning for Robotics, VDM-Verlag, ISBN 978-3-639-02110-3. [PDF]

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]

Piater, J.; Jodogne, S.; Detry, R.; Kraft, D.; Krueger, N.; Kroemer, O.; Peters, J. (in press). Learning Visual Representations for Perception-Action Systems, International Journal of Robotics Research.

Nguyen-Tuong, D.; Peters, J. (in press). Incremental Sparsification for Real-time Online Model Learning, Neurocomputing.

Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (in press). Combining Active Learning and Reactive Control for Robot Grasping , Robotics and Autonomous Systems. [PDF]

Lampert, C.H.; Peters, J. (in press). Real-Time Detection of Colored Objects In Multiple Camera Streams With Off-the-Shelf Hardware Components, Journal of Real-Time Image Processing. [PDF]

Chiappa, S.; Peters, J. (2011). Movement extraction by detecting dynamics switches and repetitions, Advances in Neural Information Processing Systems 24 (NIPS 2010), Cambridge, MA: MIT Press.

Alvaro, M.; Peters, J.; Schoelfkopf, S.; Lawrence, N. (2011). Switched Latent Force Models for Movement Segmentation, Advances in Neural Information Processing Systems 24 (NIPS 2010), Cambridge, MA: MIT Press.

Wierstra, D.; Foerster, A.; Peters, J.; Schmidhuber, J. (2010). Recurrent Policy Gradients, Logic Journal of the IGPL, 18, pp.620-634. [PDF]

Sehnke, F.; Osendorfer, C.; Rueckstiess, T.; Graves, A.; Peters, J.; Schmidhuber, J. (2010). Parameter-exploring Policy Gradients, Neural Networks, 23. [PDF]

Morimura, T.; Uchibe, E.; Yoshimoto, J.; Peters, J.; Doya, K. (2010). Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning, Neural Computation, 22, 2. [PDF]

Hachiya,H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2009). Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning, Neural Networks, 22, 10, pp.1399-1410.
[Keywords: off-policy reinforcement learning; value function approximation; policy iteration; adaptive importance sampling; importance-weighted cross-validation; efficient sample reuse] [PDF]

Deisenroth, M.P., Rasmussen, C.E.; Peters, J. (2009). Gaussian Process Dynamic Programming, Neurocomputing, 72, pp.1508-1524. [PDF]

Peters, J.; Ng, A. (2009). Guest Editorial: Special Issue on Robot Learning, Part B, Autonomous Robots, 27, 2. [PDF]

Nguyen-Tuong, D.; Seeger, M.; Peters, J. (2009). Model Learning with Local Gaussian Process Regression, Advanced Robotics, 23, 15, pp.2015-2034. [PDF]

Kober, J.; Peters, J. (2009). Reinforcement Learning fuer Motor-Primitive, Kuenstliche Intelligenz. [PDF]

Peters, J.; Morimoto, J.; Tedrake, R.; Roy, N. (2009). Robot Learning, IEEE Robotics & Automation Magazine, 16, 3, pp.19-20.
[Keywords: robot learning, tc spotlight] [PDF]

Peters, J.; Ng, A. (2009). Guest Editorial: Special Issue on Robot Learning, Part A, Autonomous Robots, 27, 1. [PDF]

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

Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2008). Operational space control: A theoretical and emprical comparison, International Journal of Robotics Research, 27, 6, pp.737-757.
[Keywords: task space control, operational space control, redundancy resolution, humanoid robotics] [PDF]

Peters, J. (2008). Machine Learning for Motor Skills in Robotics, Kuenstliche Intelligenz, 3.
[Keywords: motor control, motor primitives, motor learning] [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] [PDF]

Peters, J.;Schaal, S. (2008). Learning to control in operational space, International Journal of Robotics Research, 27, pp.197-212.
[Keywords: operational space control, learning, EM ALGORITHM, redundancy resolution, reinforcement learning] [PDF]

Peters, J.;Schaal, S. (2008). Reinforcement learning of motor skills with policy gradients, Neural Networks, 21, 4, pp.682-97.
[Keywords: Reinforcement learning, Policy gradient methods, Natural gradients, Natural Actor-Critic, Motor skills, Motor primitives] [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.
[Keywords: operational space control, inverse control, dexterous manipulation, optimal control] [PDF]

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

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

Kober, J.; Oztop, E.; Peters, J. (2010). Reinforcement Learning to adjust Robot Movements to New Situations, Proceedings of Robotics: Science and Systems (R:SS). [PDF]

Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2010). Adapting Preshaped Grasping Movements using Vision Descriptors, From Animals to Animats 11 – International Conference on the Simulation of Adaptive Behavior (SAB). [PDF]

Kroemer, O.; Detry, R.; Piater, J.; Peters, J. (2010). Grasping with Vision Descriptors and Motor Primitives, Proceedings of the International Conference on Informatics in Control, Automation and Robotics (ICINCO). [PDF]

Muelling, K.; Kober, J.; Peters, J. (2010). Simulating Human Table Tennis with a Biomimetic Robot Setup, From Animals to Animats 11 – International Conference on the Simulation of Adaptive Behavior (SAB). [PDF]

Nguyen-Tuong, D.; Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning, Proceedings of Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010). [PDF]

Peters, J.; Muelling, K.; Altun, Y. (2010). Relative Entropy Policy Search, Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence (AAAI), Physically Grounded AI Track. [PDF]

Kober,J.; Muelling,K.; Kroemer, O.; Lampert,C.H.; Schoelkopf, B.; Peters, J. (2010). Movement Templates for Learning of Hitting and Batting, IEEE International Conference on Robotics and Automation. [PDF]

Nguyen-Tuong, D.; Peters, J. (2010). Using Model Knowledge for Learning Inverse Dynamics, IEEE International Conference on Robotics and Automation. [PDF]

Erkan,A.: Kroemer,O.; Detry,R.; Altun,Y.; Piater,J.; Peters,J. (2010). Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [PDF]

Muelling, K.; Kober, J.; Peters, J. (2010). A Biomimetic Approach to Robot Table Tennis, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [PDF]

Gomez-Rodriguez, M.; Grosse-Wentrup, M.; Peters, J.; Naros, G.; Hill, J.; Gharabaghi, A.; Schoelkopf, B. (2010). Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis, 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging. [PDF]

Gomez-Rodriguez, M.; Peters, J.; Hill, J.; Schoelkopf, B.; Gharabaghi, A.; Grosse-Wentrup, M. (2010). Closing the Sensorimotor Loop: Haptic Feedback Facilitates Decoding of Arm Movement Imagery, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (Workshop on Brain-Machine Interfaces). [PDF]

Gomez Rodriguez, M.; Peters, J.; Hill, J.; Gharabaghi, A.; Schoelkopf, B.; Grosse-Wentrup, M. (2010). BCI and robotics framework for stroke rehabilitation, Proceedings of the 4th International BCI Meeting, May 31 - June 4, 2010. Asilomar, CA, USA. [PDF]

Hachiya, H.; Peters, J.; Sugiyama, M. (2009). Efficient Sample Reuse in EM-based Policy Search, Proceedings of the 16th European Conference on Machine Learning (ECML 2009). [PDF]

Peters, J.; Kober, J.; Muelling, K.; Nguyen-Tuong, D.; Kroemer, O. (2009). Towards Motor Skill Learning for Robotics, Proceedings of the International Symposium on Robotics Research (ISRR), Invited Paper. [PDF]

Nguyen-Tuong, D.; Seeger, M.; Peters, J. (2009). Local Gaussian Process Regression for Real Time Online Model Learning and Control, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]

Neumann, G.; Peters, J. (2009). Fitted Q-iteration by Advantage Weighted Regression, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]

Kober, J.; Peters, J. (2009). Policy Search for Motor Primitives in Robotics, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]

Chiappa, S.; Kober, J.; Peters, J. (2009). Using Bayesian Dynamical Systems for Motion Template Libraries, Advances in Neural Information Processing Systems 22 (NIPS 2008), Cambridge, MA: MIT Press. [PDF]

Hoffman, M.; de Freitas, N. ; Doucet, A.; Peters, J. (2009). An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AIStats). [PDF]

Peters, J.; Kober, J. (2009). Using Reward-Weighted Imitation for Robot Reinforcement Learning, Proceedings of the 2009 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.. [PDF]

Hachiya, H.; Akiyama, T.; Sugiyama, M.; Peters, J. (2009). Efficient Data Reuse in Value Function Approximation, Proceedings of the 2009 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.. [PDF]

Kober, J.; Peters, J. (2009). Learning Motor Primitives for Robotics, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). [PDF]

Piater, J.; Jodogne, S.; Detry, R.; Kraft, D.; Krueger, N.; Kroemer, O.; Peters, J. (2009). Learning Visual Representations for Interactive Systems, Proceedings of the International Symposium on Robotics Research (ISRR), Invited Paper. [PDF]

Kober, J., and Peters, J. (2009). Learning new basic Movements for Robotics , Proceedings of Autonome Mobile Systeme (AMS 2009). [PDF]

Muelling, K., and Peters, J. (2009). A computational model of human table tennis for robot application, Proceedings of Autonome Mobile Systeme (AMS 2009). [PDF]

Kroemer, O., Detry, R., Piater, J., and Peters, J. (2009). Active Learning Using Mean Shift Optimization for Robot Grasping, Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009). [PDF]

Nguyen-Tuong, D., Schoelkopf, B., and Peters, J. (2009). Sparse Online Model Learning for Robot Control with Support Vector Regression, Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009). [PDF]

Sigaud, O.; Peters, J. (2009). From Motor Learning to Interaction Learning in Robots, Proceedings of Journees Nationales de la Recherche en Robotique. [PDF]

Neumann, G.; Maass, W; Peters, J. (2009). Learning Complex Motions by Sequencing Simpler Motion Templates, Proceedings of the International Conference on Machine Learning (ICML2009). [PDF]

Detry, R; Baseski, E.; Popovic, M.; Touati, Y.; Krueger, N; Kroemer, O.; Peters, J.; Piater, J; (2009). Learning Object-specific Grasp Affordance Densities, Proceedings of the International Conference on Development & Learning (ICDL 2009). [PDF]

Lampert, C.H.; Peters, J. (2009). Active Structured Learning for High-Speed Object Detection, Proceedings of the DAGM (Pattern Recognition). [PDF]

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

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

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). [PDF]

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). [PDF]

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

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). [PDF]

Wierstra,D.; Schaul,T.; Peters, J.; Schmidhuber, J. (2008). Natural Evolution Strategies, 2008 IEEE Congress on Evolutionary Computation. [PDF]

Nguyen-Tuong, D.; Peters, J. (2008). Local Gaussian Processes Regression for Real-time Model-based Robot Control, International Conference on Intelligent Robot Systems (IROS). [PDF]

Kober, J.; Mohler, B.; Peters, J. (2008). Learning Perceptual Coupling for Motor Primitives, International Conference on Intelligent Robot Systems (IROS). [PDF]

Wierstra, D.; Schaul, T.; Peters, J.; Schmidthuber, J. (2008). Fitness Expectation Maximization, 10th International Conference on Parallel Problem Solving from Nature (PPSN 2008). [PDF]

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). [PDF]

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). [PDF]

Peters, J.; Kober, J.; Nguyen-Tuong, D. (2008). Policy Learning – a unified perspective with applications in robotics, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
[Keywords: reinforcement learning, policy gradient, weighted regression] [PDF]

Kober, J.; Peters, J. (2008). Reinforcement Learning of Perceptual Coupling for Motor Primitives, Proceedings of the European Workshop on Reinforcement Learning (EWRL).

Nguyen-Tuong, D.; Peters, J. (2008). Learning Robot Dynamics for Computed Torque Control using Local Gaussian Processes Regression, Proceedings of the ECSIS Symposium on Learning and Adaptive Behavior in Robotic Systems, LAB-RS 2008. [PDF]

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] [PDF]

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] [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] [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]

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] [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] [PDF]

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

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

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

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

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]