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In the past decades, various mathematical and physical models have been developed to predict and control mechanical systems. However, they are not powerful in some complex tasks such as analysis of human biosignal and visual information for autonomous systems.

Our goal is to address this problem with AI techniques, and we are currently focusing on;

  • Gait estimation model for wearable robots

  • Visual ground recognition system for wearable robots

  • Object/texture classifiable upper prosthesis

  • Machine learning algorithms for electromyography

  • Autonomous control of unmanned aerial vehicles

  • Visual tracking of micro aerial vehicles

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