Hand on Robotics

Hands-on robotics research focuses on improving human-robot interaction and education through direct physical engagement. Current efforts concentrate on developing efficient methods for robot parameter identification, using techniques like recursive Newton-Euler algorithms and optimizing excitation trajectories to avoid self-collision, as well as creating intuitive interfaces for gesture-based control leveraging machine learning for accurate gesture recognition. This work is significant for advancing both the usability and accessibility of robotics, impacting fields ranging from industrial automation and surgery to STEM education by providing more effective training methods and fostering broader participation.