Papers
1
Total Citations
2
H-Index
1
About
Hosun Lee’s research lies at the intersection of developmental robotics, active perception, and sensorimotor learning, with a focus on how biological systems self-calibrate without external supervision. In his most-cited work, “Self-calibrating active depth perception via motion parallax” (2016, 2 citations), Lee explores how agents can autonomously learn depth perception by exploiting motion parallax—a fundamental cue used by animals. This study demonstrates a principled framework for robots to develop sensory-motor coordination through self-generated movement, eliminating the need for pre-calibrated models or human intervention. By drawing inspiration from infant development, Lee’s work contributes to a deeper understanding of how autonomous learning can emerge from simple, embodied interactions. Though still early in its citation impact, this research is conceptually significant for fields like neurorobotics and developmental psychology, offering a pathway toward more adaptive and self-sufficient robotic systems. Lee’s approach emphasizes minimal assumptions and maximal biological plausibility, positioning his work as a thoughtful bridge between computational models and natural intelligence.
Research Focus
Key Achievements
Top Papers
- 1Self-calibrating active depth perception via motion parallax2 citations · 2016