Matěj Hoffmann presents Robot in a crib: how a playing robot helps us understand sensorimotor contingency learning
On 2026-09-10 - 2026-09-10 11:00:00 at E112, Karlovo náměstí 13, Praha 2
Learning sensorimotor contingencies---that is, the link between one's actions
and their sensory effects---is fundamental to developing body knowledge,
understanding causality, and 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 test 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 detects sensorimotor contingencies across several experimental
conditions by employing 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.
and their sensory effects---is fundamental to developing body knowledge,
understanding causality, and 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 test 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 detects sensorimotor contingencies across several experimental
conditions by employing 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.