Papers
1
Total Citations
30
H-Index
1
About
Veljko Krunic is a researcher whose work sits at the intersection of robotics, cognitive science, and language acquisition, with a particular focus on how robots can learn meaning through interaction. His most cited work, "Affordance based word-to-meaning association" (2009, 30 citations), introduces a novel framework that enables robots to link words to their meanings by grounding language in physical actions and perceptions. By extending the affordance model—a mapping between robot actions, perceptions, and their effects on objects—Krunic demonstrates how machines can autonomously associate linguistic labels with the functional properties of objects, moving beyond static symbol grounding. This contribution is significant for advancing developmental robotics and human-robot interaction, offering a pathway for robots to learn language as humans do: through embodied experience. While his citation count reflects a focused, early-career impact, his work is notable for bridging affordance theory with computational linguistics, inspiring further research in grounded language learning and cognitive robotics. Krunic’s approach remains a key reference for those exploring how robots can acquire semantic knowledge from their environment.
Research Focus
Key Achievements
Top Papers
- 1Affordance based word-to-meaning association30 citations · 2009