Hyeonwoo Noh
Papers
1
Total Citations
21
H-Index
1
About
Hyeonwoo Noh is a leading researcher in robotics and artificial intelligence, with a primary focus on goal-conditioned reinforcement learning and autonomous skill acquisition. His most influential work introduces a novel framework called asymmetric self-play for automatic goal discovery in robotic manipulation. In this approach, two agents—Alice and Bob—engage in a cooperative game where Alice proposes increasingly challenging goals for Bob to solve, enabling the system to autonomously generate a curriculum of tasks without human intervention. This method allows a single, goal-conditioned policy to master a wide variety of manipulation tasks, including those involving previously unseen objects and goals. The paper has garnered 21 citations since its 2021 publication, reflecting its impact on the field. Noh’s contributions are particularly notable for advancing the scalability and autonomy of robotic learning systems, reducing the need for manual task design. His work stands out for its elegant combination of self-play and goal discovery, offering a practical path toward more general-purpose robots.
Research Focus
Key Achievements
Top Papers
- 1Asymmetric self-play for automatic goal discovery in robotic manipulation21 citations · 2021