Papers
92
Total Citations
7,181
H-Index
31
About
Jens Kober is a prominent robotics and machine learning researcher whose work sits at the intersection of reinforcement learning, motor skill acquisition, and robotic manipulation. He is perhaps best known for his landmark 2013 survey, "Reinforcement Learning in Robotics," which has accumulated over 3,000 citations and remains one of the most authoritative references in the field, comprehensively mapping how reinforcement learning frameworks can address the unique challenges of robotic control. A central theme throughout Kober's career is the development of motor primitives — reusable, adaptable movement building blocks that allow robots to learn and generalize complex skills efficiently. His influential work on policy search methods and parametrized motor plans demonstrated how robots can transfer learned movements to new situations without relearning from scratch, drawing inspiration from human motor adaptation. His robot table tennis research vividly illustrated these principles in a dynamic, real-world setting. More recently, Kober has expanded his focus to deformable object manipulation, a challenging frontier where rigid-body assumptions break down, contributing a widely-cited 2022 outlook that is shaping emerging research directions. With a citation record exceeding 5,000 across his top works, Kober's contributions have profoundly influenced how modern robots learn, adapt, and interact with their environments.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning in robotics: A survey3,055 citations · 2013
- 2Policy search for motor primitives in robotics437 citations · 2010
- 3Learning to select and generalize striking movements in robot table tennis404 citations · 2013
- 4Challenges and Outlook in Robotic Manipulation of Deformable Objects244 citations · 2022
- 5Policy Search for Motor Primitives in Robotics233 citations · 2013
- 6Reinforcement Learning in Robotics: A Survey216 citations · 2013
- 7Reinforcement learning to adjust parametrized motor primitives to new situations161 citations · 2012
- 8Reinforcement Learning in Robotics: A Survey148 citations · 2012
- 9Reinforcement Learning to adjust Robot Movements to New Situations136 citations · 2010
- 10Reinforcement learning based compensation methods for robot manipulators122 citations · 2018