  Humans are very effective at interpreting subtle properties of the partner's
movement and use this skill to promote smooth interactions. Therefore, robotic
platforms that support human partners in daily activities should acquire
similar abilities. In this work we focused on the features of human motor
actions that communicate insights on the weight of an object and the
carefulness required in its manipulation. Our final goal is to enable a robot
to autonomously infer the degree of care required in object handling and to
discriminate whether the item is light or heavy, just by observing a human
manipulation. This preliminary study represents a promising step towards the
implementation of those abilities on a robot observing the scene with its
camera. Indeed, we succeeded in demonstrating that it is possible to reliably
deduct if the human operator is careful when handling an object, through
machine learning algorithms relying on the stream of visual acquisition from
either a robot camera or from a motion capture system. On the other hand, we
observed that the same approach is inadequate to discriminate between light and
heavy objects.
