Jan Peters
Technische Universität Darmstadt, University of Southern California, Max Planck Institute for Biological Cybernetics, Max Planck Society, Max Planck Institute for Intelligent Systems, Institut des Sciences Cognitives Marc Jeannerod, Bielefeld University, Max Planck Institute for Biology, Hess (United States), Tokyo Institute of Technology, National Technical University of Athens, German Research Centre for Artificial Intelligence, Fraunhofer Institute for Mechatronic Systems Design, KTH Royal Institute of Technology
Papers
360
Total Citations
22,544
H-Index
73
About
Jan Peters is a pioneering researcher at the intersection of machine learning and robotics, whose work has fundamentally shaped how autonomous systems learn and execute complex motor behaviors. Best known for his landmark 2013 survey on reinforcement learning in robotics — now cited over 3,000 times — Peters has spent his career bridging the gap between theoretical machine learning and real-world robotic applications. His foundational contributions to policy gradient methods, including the influential Natural Actor-Critic framework and seminal work on policy gradients for robotics (collectively exceeding 1,300 citations), established core algorithmic tools that researchers worldwide continue to build upon. Peters has also advanced the field through probabilistic movement primitives and motor skill learning, enabling robots to acquire, generalize, and adapt behaviors in unstructured environments — demonstrated memorably through robot table tennis. His surveys on model learning for robot control and imitation learning reflect a commitment to synthesizing and guiding the broader research community. With multiple papers exceeding hundreds of citations and a body of work spanning reinforcement learning, imitation learning, and operational space control, Peters stands as one of the most influential figures in modern robot learning research.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning in robotics: A survey3,055 citations · 2013
- 2Reinforcement learning of motor skills with policy gradients863 citations · 2008
- 3Natural Actor-Critic751 citations · 2008
- 4Policy Gradient Methods for Robotics519 citations · 2006
- 5Model learning for robot control: a survey499 citations · 2011
- 6Policy search for motor primitives in robotics437 citations · 2010
- 7Probabilistic Movement Primitives413 citations · 2013
- 8Learning to select and generalize striking movements in robot table tennis404 citations · 2013
- 9Operational Space Control: A Theoretical and Empirical Comparison383 citations · 2008
- 10An Algorithmic Perspective on Imitation Learning379 citations · 2018