Sungwoo Yang
Papers
5
Total Citations
20
H-Index
3
About
Sungwoo Yang is a robotics researcher advancing the frontier of socially aware human-robot interaction. His work centers on developing intelligent navigation and manipulation systems that enable robots to coexist safely and naturally with humans in shared environments. Yang’s most impactful contribution is the "Transformable Gaussian Reward Function" for deep reinforcement learning, which achieved 8 citations by teaching robots to navigate crowds with social awareness. He further introduced the "Social Type-Aware Navigation Framework," recognizing that individuals have different spatial preferences during interactions. Yang also developed the NUMMIC controller, a universal system for coordinating mobile platforms and manipulator arms, and explored reinforcement learning for safe human-to-robot handovers using anthropomorphic grippers. To accelerate research, he implemented a reinforcement learning environment for mobile manipulators using Robo-gym, enabling reproducible experiments. With over 20 total citations across his key papers, Yang is establishing himself as a rising voice in socially compliant robotics, bridging the gap between theoretical reinforcement learning and practical, human-aware robot behavior.
Research Focus
Key Achievements
Top Papers
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- 4Human-to-Robot Handover Based on Reinforcement Learning2 citations · 2024
- 5