Papers
8
Total Citations
83
H-Index
5
About
Georgios Pierris is a robotics researcher whose work spans human-robot interaction, machine learning, and cognitive architectures. His key research areas include robot motion programming, teleoperation, hierarchical learning, and human-machine systems. Pierris made significant contributions by developing the Kouretes Motion Editor (KME), an interactive tool that democratizes robot programming by enabling non-experts to design complex motion patterns for low-cost, high-degree-of-freedom robots (18 citations). His work on joystick-based robot arm teleoperation with auditory feedback (13 citations each) addresses critical challenges in training and assessing human operators for applications ranging from excavators to space telerobotics. Pierris also advanced hierarchical learning through the Compressed Sparse Code Hierarchical Self-Organizing Map (CoSCo-HSOM) algorithm, which enables humanoid robots to learn and reproduce gestures (12 citations). His research on the Human-Robot Cloud framework (13 citations) explores distributed, on-demand cognitive systems for smart buildings. Pierris’s work on developmental skill learning using hierarchical SOM-based encoding (5 citations) provides insights into how robots can acquire motor skills through demonstration rather than hand-coding, mirroring human developmental processes.
Research Focus
Key Achievements
Top Papers
- 1An interactive tool for designing complex robot motion patterns18 citations · 2009
- 2
- 3
- 4Smart buildings and the human-machine cloud13 citations · 2015
- 5
- 6
- 7Learning Robot Control Using a Hierarchical SOM-Based Encoding5 citations · 2017
- 8