Authors
Josua Spisak, Sergiu Tcaci Popescu, Lukas Rustler, Stefan Wermter, J Kevin O'Regan, Matej Hoffmann
Published in
Science robotics. Volume 11. Issue 117. Pages eaed4106. Aug 26, 2026. Epub Aug 26, 2026.
Abstract
Learning sensorimotor contingencies-that is, the link between one's actions and their sensory effects-is fundamental to developing body knowledge, understanding causality, and developing a sense of agency. In developmental psychology, this process is classically studied using the mobile paradigm, where infants learn that movement of a limb causes motion of a connected mobile. To expand our understanding of how infants learn this, we tested an embodied computational model that learns through two biologically inspired mechanisms: prediction and curiosity. Implemented on the child-sized iCub humanoid robot interacting with a mobile, the model detected sensorimotor contingencies across several experimental conditions using a variety of movement strategies. Our findings suggest that contingency learning cannot be captured by a single behavioral metric, such as the amount of movement, but instead emerges through a spectrum of exploratory behaviors. Analysis of the robot's internal activity reveals that these behaviors emerge from the dynamic trade-off between prediction and curiosity-between exploitation and exploration. Our work provides a biologically motivated, physically embodied model of sensorimotor interaction that connects theories of infant learning with robotic implementations. The results allow us to generate testable hypotheses for developmental research and to inform the design of autonomous learning systems.
PMID:
42647591
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.
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