Augustin Chartouny
Papers
2
Total Citations
5
H-Index
2
About
Augustin Chartouni is a rising researcher at the intersection of cognitive robotics, developmental psychology, and machine learning. His work focuses on how artificial agents can learn to perceive and exploit affordances—action possibilities offered by the environment—with a particular emphasis on social affordances in human-robot interaction. Chartouni’s 2024 paper, “A new paradigm to study social and physical affordances as model-based reinforcement learning,” introduces a framework that extends affordance learning from purely physical contexts to social ones, addressing a critical gap in robotics. This work has already garnered 3 citations, signaling its early impact. In a commentary published in *Trends in Cognitive Sciences*, Chartouni critically engages with computational models of curiosity, arguing that learning progress mechanisms require absolute value signals to avoid maladaptive exploration—a nuance often overlooked in cognitive neuroscience. This contribution, with 2 citations, demonstrates his ability to bridge machine learning theory and human cognition. Chartouni’s research is notable for its interdisciplinary rigor, offering concrete computational paradigms that could shape how robots learn from and interact with humans. His work is essential reading for anyone interested in developmental robotics, intrinsic motivation, or social AI.
Research Focus
Key Achievements
Top Papers
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- 2