Tanapol Prucksakorn

About

Tanapol Prucksakorn is a researcher whose work lies at the intersection of robotics, computer vision, and biologically inspired perception. His primary research focus is on active depth perception, specifically exploring how autonomous systems can learn to perceive depth through motion and self-calibration—mimicking the natural development of biological sensory-motor systems. Prucksakorn’s most notable contributions include developing a self-trainable depth perception method that leverages eye pursuit and motion parallax, as well as a self-calibrating active depth perception framework. These works, though early in their citation history (with 3 and 2 citations respectively), address a fundamental challenge in robotics: enabling machines to autonomously learn depth perception without external calibration, much like living organisms. His 2016 paper on self-calibrating active depth perception is particularly significant for its principled approach to understanding how sensory-motor loops can self-tune during development, offering a pathway toward more adaptive and autonomous robots. Prucksakorn’s research is a compelling blend of neuroscience principles and engineering innovation, promising to advance the field of developmental robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A self-trainable depth perception method from eye pursuit and motion parallax

   3 citations · 2018
2. 2
   Self-calibrating active depth perception via motion parallax

   2 citations · 2016

Key Collaborators

Contact & Links