  Crossmodal conflict resolution is crucial for robot sensorimotor coupling
through the interaction with the environment, yielding swift and robust
behaviour also in noisy conditions. In this paper, we propose a neurorobotic
experiment in which an iCub robot exhibits human-like responses in a complex
crossmodal environment. To better understand how humans deal with multisensory
conflicts, we conducted a behavioural study exposing 33 subjects to congruent
and incongruent dynamic audio-visual cues. In contrast to previous studies
using simplified stimuli, we designed a scenario with four animated avatars and
observed that the magnitude and extension of the visual bias are related to the
semantics embedded in the scene, i.e., visual cues that are congruent with
environmental statistics (moving lips and vocalization) induce the strongest
bias. We implement a deep learning model that processes stereophonic sound,
facial features, and body motion to trigger a discrete behavioural response.
After training the model, we exposed the iCub to the same experimental
conditions as the human subjects, showing that the robot can replicate similar
responses in real time. Our interdisciplinary work provides important insights
into how crossmodal conflict resolution can be modelled in robots and
introduces future research directions for the efficient combination of sensory
observations with internally generated knowledge and expectations.
