Papers
9
Total Citations
100
H-Index
7
About
Woo Young Kwon is a leading researcher in human-robot interaction, specializing in proactive robotics and autonomous decision-making under uncertainty. His foundational work centers on developing intelligent robotic assistants that can predict human actions and prepare preemptive behaviors, dramatically reducing interaction wait times and improving collaborative efficiency. Kwon’s most influential contribution is the creation of probabilistic frameworks—including temporal Bayesian networks and hybrid temporal influence diagrams—that allow robots to forecast future events and plan proactive actions in real-time. His seminal paper, "Planning of proactive behaviors for human–robot cooperative tasks under uncertainty" (2014), has garnered 34 citations, while his earlier work on temporal Bayesian networks for proactive assistance (2012) has been cited 20 times. Kwon also advanced reinforcement learning with his stochastic shortest path-based Q-learning algorithm (SSPQL, 2011, 12 citations). His research extends to ethology-inspired action selection mechanisms and imitation learning for bimanual manipulation, addressing both position and force control. Through these contributions, Kwon has established a robust theoretical and practical foundation for creating truly anticipatory, human-aware robotic assistants capable of seamless cooperation in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3SSPQL: Stochastic shortest path-based Q-learning12 citations · 2011
- 4
- 5Towards proactive assistant robots for human assembly tasks7 citations · 2011
- 6
- 7
- 8
- 9Evaluating movement skills from extended neural complexity2 citations · 2012