Seok Hyun Jin
Papers
2
Total Citations
36
H-Index
2
About
Seok Hyun Jin is a roboticist whose research sits at the intersection of manipulation, machine learning, and human-robot interaction, with a particular focus on enabling robots to operate autonomously in unstructured human environments. His most influential work centers on the Robobarista project, which addresses the fundamental challenge of teaching robots to manipulate novel objects without explicit programming for every instance. In his 2016 paper (13 citations), Jin introduced a deep multimodal embedding approach that allows robots to learn manipulation trajectories from crowd-sourced data, effectively transferring knowledge across different objects like stoves and coffee dispensers. He extended this in his 2017 work (23 citations) by developing object part-based transfer methods for 3D pointclouds, enabling robots to generalize manipulation skills by recognizing functional parts rather than entire objects. This body of work has been foundational for the field of robot learning from demonstration, demonstrating how large-scale human data can be leveraged to create more adaptable and capable robotic systems. Jin’s contributions continue to influence research in robotic manipulation and autonomous task execution.
Research Focus
Key Achievements
Top Papers
- 1
- 2