Iason Batzianoulis

About

Iason Batzianoulis is a leading researcher at the intersection of robotics, brain-computer interfaces (BCI), and assistive technology. His work focuses on developing intelligent, human-centered robotic systems that can learn from and adapt to their users. A central theme of his research is the use of physiological signals—particularly electroencephalography (EEG) and electromyography (EMG)—to decode human intent and preference. His most cited work (2021, 54 citations) pioneers a method for customizing assistive robotic manipulators by combining inverse reinforcement learning with error-related potentials from EEG, enabling robots to learn user-specific preferences during operation. Batzianoulis has also made significant contributions to prosthetic control, developing an EMG-driven shared control framework for dexterous in-hand object manipulation (2022, 16 citations). His work on inferring subjective preferences from EEG signals (2019, 20 citations) and structured prediction for robot imitation learning (2023, 15 citations) further demonstrates his impact on creating more intuitive, responsive human-robot collaboration. By bridging cognitive neuroscience and robotics, Batzianoulis is advancing a future where assistive robots seamlessly understand and anticipate human needs.

Research Focus

Key Achievements

Top Papers

1. 1
   Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials

   54 citations · 2021
2. 2
   Inferring subjective preferences on robot trajectories using EEG signals

   20 citations · 2019
3. 3
   EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses

   16 citations · 2022
4. 4
   A structured prediction approach for robot imitation learning

   15 citations · 2023
5. 5
   EMG-Based Analysis of the Upper Limb Motion

   2 citations · 2015

Key Collaborators

Contact & Links