  When humans see a scene, they can roughly imagine the forces applied to
objects based on their experience and use them to handle the objects properly.
This paper considers transferring this "force-visualization" ability to robots.
We hypothesize that a rough force distribution (named "force map") can be
utilized for object manipulation strategies even if accurate force estimation
is impossible. Based on this hypothesis, we propose a training method to
predict the force map from vision. To investigate this hypothesis, we generated
scenes where objects were stacked in bulk through simulation and trained a
model to predict the contact force from a single image. We further applied
domain randomization to make the trained model function on real images. The
experimental results showed that the model trained using only synthetic images
could predict approximate patterns representing the contact areas of the
objects even for real images. Then, we designed a simple algorithm to plan a
lifting direction using the predicted force distribution. We confirmed that
using the predicted force distribution contributes to finding natural lifting
directions for typical real-world scenes. Furthermore, the evaluation through
simulations showed that the disturbance caused to surrounding objects was
reduced by 26 % (translation displacement) and by 39 % (angular displacement)
for scenes where objects were overlapping.
