Gerald Baulig
Papers
1
Total Citations
9
H-Index
1
About
Gerald Baulig’s research sits at the intersection of human-robot interaction, computer vision, and assistive robotics, with a particular focus on enabling intuitive control of exoskeletons. His most-cited work, “Adapting Egocentric Visual Hand Pose Estimation Towards a Robot-Controlled Exoskeleton” (2019, 9 citations), exemplifies his core contribution: bridging the gap between egocentric perception and robotic actuation. In this paper, Baulig developed a method to adapt hand pose estimation from a head-mounted camera to directly control an exoskeleton, effectively allowing a user’s natural hand movements to guide a robot in real time. This approach addresses a critical challenge in assistive technology—how to make robotic systems responsive to subtle, human gestures without cumbersome sensors. While his citation count reflects a focused, emerging impact, Baulig’s work is notable for its practical, user-centered design, demonstrating a clear pathway from visual data to physical assistance. His research holds promise for applications in rehabilitation and industrial support, where seamless human-robot collaboration is essential. For students and researchers in robotics and human-computer interaction, Baulig’s work offers a compelling example of how vision-based methods can be repurposed for intuitive, non-invasive control of wearable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1