Although many tasks intrinsically involve multiple
modalities, often only data from a single modality are used to
improve complex robots acquisition of new skills. We present
a method to equip robots with multimodal learning skills to
achieve multimodal imitation on-the-fly on multiple concurrent
task spaces, including vision, touch and proprioception, only
using self-learned multimodal sensorimotor relations, without
the need of solving inverse kinematic problems or explicit analytical
models formulation. We evaluate the proposed method
on a humanoid iCub robot learning to interact with a piano
keyboard and imitating a human demonstration. Since no
assumptions are made on the kinematic structure of the robot,
the method can be also applied to different robotic platforms.