Jihwan Kim
Papers
5
Total Citations
130
H-Index
3
About
Jihwan Kim is a robotics researcher whose work focuses on enabling safer, more capable robot manipulation and motion in human-centered environments. His primary research areas include collision detection, grasping, motion planning, and cable-driven parallel robots. Kim’s most impactful contribution is a learning-based method for real-time detection of both hard and soft robot collisions without the need for expensive joint torque sensors—a critical advance for human-robot interaction, earning 97 citations. He also developed DSQNet, a deformable model-based supervised learning algorithm for grasping unknown, occluded objects, addressing a key limitation of data-hungry deep learning approaches. In motion planning, Kim introduced an active learning framework for efficiently computing collision distance functions for high-degree-of-freedom multi-arm systems. His work on a three-cable driven planar parallel robot for upper-arm exercise demonstrates his interest in practical rehabilitation applications, and he has also tackled the real-world challenge of mobile robots safely navigating elevators using RGB-D sensors. Through these contributions, Kim is advancing the frontier of robots that can work safely alongside people in unstructured, dynamic spaces.
Research Focus
Key Achievements
Top Papers
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