Soomin Kang
Papers
1
Total Citations
2
H-Index
1
About
Soomin Kang is a robotics researcher whose work centers on bridging the gap between simulation and real-world robotic control, with a particular focus on reinforcement learning for mobile manipulators. Her most-cited paper, "Implementation of Reinforcement Learning Environment for Mobile Manipulator Using Robo-gym" (2022), addresses a critical bottleneck in the field: the scarcity of ready-to-use reinforcement learning environments for specific robot platforms. By developing and integrating a custom environment for mobile manipulators within the Robo-gym framework, Kang has provided a vital tool that enables other researchers to train and test control policies without the prohibitive costs and risks of physical hardware. This contribution, which has already garnered 2 citations, underscores her commitment to democratizing access to advanced robotics research tools. Her work is particularly notable for its practical orientation, ensuring that simulation environments are not just theoretically sound but immediately deployable. Through this effort, Soomin Kang is helping to accelerate the development of more capable, autonomous robots that can learn complex manipulation and navigation tasks in safe, simulated spaces before being deployed in the real world.
Research Focus
Key Achievements
Top Papers
- 1