Hsiang‐Ting Chen
Papers
3
Total Citations
32
H-Index
3
About
Hsiang‐Ting Chen is a leading researcher at the intersection of brain–computer interfaces (BCIs) and human–robot interaction, with a focus on making robot control more intuitive and adaptive. Their most influential work centers on error-related potential (ErrP)-based BCIs, where neural signals automatically triggered by unexpected machine behavior are harnessed to guide robots without requiring explicit user commands. Chen’s 2022 paper on implicit robot control using ErrP-based BCIs (19 citations) demonstrates how these neural signals can enable seamless, hands-free robot operation. Building on this, their work on ErrP-based shared autonomy via deep recurrent reinforcement learning (8 citations) introduces a powerful framework where robots learn to interpret and act upon these brain signals in real time, blending human intent with machine autonomy. Earlier, Chen contributed to service robotics with a 3D vision-based grasping posture learning system (5 citations), enabling home service robots to autonomously determine optimal grasping strategies. With a growing citation impact and a clear trajectory toward more natural, brain-driven human–robot collaboration, Chen is shaping the future of assistive and interactive robotics.
Research Focus
Key Achievements
Top Papers
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