Timo Korthals
Papers
12
Total Citations
106
H-Index
7
About
Timo Korthals is a robotics researcher whose work sits at the intersection of deep reinforcement learning, modular hardware design, and multi-robot systems. His most significant contributions include pioneering the use of tactile sensing to dramatically improve the sample efficiency of Deep Deterministic Policy Gradients for dexterous in-hand manipulation—a key step toward making simulated training more practical for real-world robotics. He is also the driving force behind the AMiRo platform, a modular, open-source mini robot that has been widely adopted for both scientific research and as a versatile teaching tool, with multiple papers detailing its architecture and applications. Korthals has advanced distributed perception through latent space representations, enabling heterogeneous robot teams to share sensor information efficiently. His work on occupancy grid mapping with highly uncertain sensors and modular deep reinforcement learning for legged locomotion further demonstrates his breadth. With over 100 citations across his top papers, Korthals has established himself as a key figure in making robotics more accessible, sample-efficient, and capable of operating in unpredictable environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2AMiRo: A modular & customizable open-source mini robot platform24 citations · 2016
- 3
- 4AMiRo: A Mini Robot as Versatile Teaching Platform9 citations · 2018
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- 7
- 8AMiRo: A Mini Robot for Scientific Applications3 citations · 2015
- 9
- 10Generic Architecture for Modular Real-time Systems in Robotics2 citations · 2018