Papers
22
Total Citations
755
H-Index
13
About
Daniel Hennes is a versatile researcher whose work spans multi-agent systems, autonomous robotics, and machine learning, with particular expertise in the intersection of these fields. He is perhaps best known for his highly influential 2015 survey on "Evolutionary Dynamics of Multi-Agent Learning," which has garnered over 287 citations and stands as a foundational reference for researchers studying how autonomous agents adapt and interact in complex, dynamic environments — with applications ranging from financial markets to smart grids and robotics. Hennes has made significant contributions to multi-robot coordination, including pioneering work on collision avoidance under real-world localization uncertainty, with two complementary 2012 papers accumulating over 170 combined citations. His research extended naturally into aerial robotics, producing an innovative 3D SLAM system for UAVs and Gaussian process methods for odometry error estimation. More recently, he has explored tactile exploration, contact-rich manipulation using reinforcement learning, and even soft robotics for space exploration — demonstrating a remarkable breadth of vision. His 2019 work on active tactile exploration with Gaussian processes and deep reinforcement learning for manipulation highlights his commitment to bridging theoretical rigor with practical robotics applications, making him a compelling figure in modern autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1Evolutionary Dynamics of Multi-Agent Learning: A Survey287 citations · 2015
- 2Multi-robot collision avoidance with localization uncertainty100 citations · 2012
- 3Collision avoidance under bounded localization uncertainty74 citations · 2012
- 4Novelty Search for Soft Robotic Space Exploration41 citations · 2015
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- 7Gaussian process estimation of odometry errors for localization and mapping28 citations · 2017
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