Yunjung Park
Papers
2
Total Citations
18
H-Index
2
About
Yunjung Park’s research lies at the intersection of cognitive robotics, neural computation, and sensorimotor learning. Her work explores how robots can develop perceptual and behavioral skills through interaction, drawing inspiration from human development. In her most cited paper, “Autonomous and Interactive Improvement of Binocular Visual Depth Estimation through Sensorimotor Interaction” (2012, 9 citations), Park investigates how a humanoid robot with a randomly initialized binocular vision system can learn to improve egocentric distance judgments using limited action and interaction—mirroring the constraints faced by human infants. This work demonstrates both autonomous and interactive pathways for perceptual learning. Her second highly cited paper, “Goal-oriented behavior sequence generation based on semantic commands using multiple timescales recurrent neural network with initial state correction” (2013, 9 citations), advances the generation of complex, goal-directed behaviors from high-level semantic commands. Park’s contributions are notable for bridging developmental psychology and robotics, offering biologically plausible models for adaptive perception and action. Her research has been cited in studies on neural network architectures, robot learning, and human-robot interaction, marking her as a thoughtful contributor to embodied cognition and autonomous systems.
Research Focus
Key Achievements
Top Papers
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