Papers
66
Total Citations
1,443
H-Index
19
About
Dimitrios Kanoulas is a robotics researcher whose work spans humanoid robot locomotion, computer vision, and robot perception, with a particular focus on enabling autonomous systems to operate in complex, unstructured real-world environments. He is perhaps best known for his contributions to the WALK-MAN humanoid platform, a high-performance robot designed for disaster response scenarios such as post-earthquake environments, which has accumulated over 260 citations and demonstrated capabilities ranging from robust locomotion to powerful manipulation. Kanoulas has made significant strides in object affordance detection, developing deep Convolutional Neural Network approaches — including encoder-decoder architectures and dense Conditional Random Fields — that allow robots to understand how objects can be used, with these works collectively garnering nearly 340 citations. His research further extends to terrain classification and segmentation, footstep planning on rough surfaces, grasp pose adaptation using center-of-mass estimation, and bi-manual teleoperation systems. More recently, he has applied whole-body control and deep learning to practical tasks such as autonomous garbage sorting with mobile manipulators. Across his portfolio, Kanoulas consistently bridges the gap between perception and physical robot action, making him a notable contributor to applied robotics research.
Research Focus
Key Achievements
Top Papers
- 1WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments266 citations · 2017
- 2Detecting object affordances with Convolutional Neural Networks175 citations · 2016
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- 7Humanoids at Work: The WALK-MAN Robot in a Postearthquake Scenario36 citations · 2018
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