  Placing objects is a fundamental task for domestic service robots (DSRs).
Thus, inferring the collision-risk before a placing motion is crucial for
achieving the requested task. This problem is particularly challenging because
it is necessary to predict what happens if an object is placed in a cluttered
designated area. We show that a rule-based approach that uses plane detection,
to detect free areas, performs poorly. To address this, we develop PonNet,
which has multimodal attention branches and a self-attention mechanism to
predict damaging collisions, based on RGBD images. Our method can visualize the
risk of damaging collisions, which is convenient because it enables the user to
understand the risk. For this purpose, we build and publish an original dataset
that contains 12,000 photo-realistic images of specific placing areas, with
daily life objects, in home environments. The experimental results show that
our approach improves accuracy compared with the baseline methods.
