Papers
12
Total Citations
553
H-Index
5
About
Yohannes Kassahun is a leading researcher at the intersection of machine learning, mobile robotics, and autonomous systems, with a particular focus on advancing both autonomous driving and surgical robotics. His most impactful contribution is the creation of the Audi Autonomous Driving Dataset (A2D2), a seminal resource that provides simultaneously recorded images and 3D point clouds with high-quality annotations. With 265 citations, A2D2 has become a cornerstone for accelerating research in perception and navigation for self-driving vehicles. In surgical robotics, Kassahun co-authored a highly influential survey (238 citations) that systematically reviewed machine learning techniques for enabling intelligent and autonomous surgical actions, moving beyond traditional enhanced dexterity instrumentation. His work also spans robot dynamics identification, where he experimentally compared classical and machine learning approaches on a KUKA iiwa robot, and legged robot motion modeling through dynamic Gaussian mixture models. Additionally, Kassahun has explored neuroevolutionary methods for automatic neural controller design and reinforcement learning for autonomous robotic catheter navigation. His research consistently bridges theoretical advances with practical, data-driven solutions, making him a key figure in the evolution of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A2D2: Audi Autonomous Driving Dataset265 citations · 2020
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- 5Dynamic motion modelling for legged robots7 citations · 2009
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- 7A General Framework for Encoding and Evolving Neural Networks5 citations · 2007
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- 10On Applying Neuroevolutionary Methods to Complex Robotic Tasks3 citations · 2011