  Open containers, i.e., containers without covers, are an important and
ubiquitous class of objects in human life. In this letter, we propose a novel
method for robots to "imagine" the open containability affordance of a
previously unseen object via physical simulations. We implement our imagination
method on a UR5 manipulator. The robot autonomously scans the object with an
RGB-D camera. The scanned 3D model is used for open containability imagination
which quantifies the open containability affordance by physically simulating
dropping particles onto the object and counting how many particles are retained
in it. This quantification is used for open-container vs. non-open-container
binary classification (hereafter referred to as open container classification).
If the object is classified as an open container, the robot further imagines
pouring into the object, again using physical simulations, to obtain the
pouring position and orientation for real robot autonomous pouring. We evaluate
our method on open container classification and autonomous pouring of granular
material on a dataset containing 130 previously unseen objects with 57 object
categories. Although our proposed method uses only 11 objects for simulation
calibration (training), its open container classification aligns well with
human judgements. In addition, our method endows the robot with the capability
to autonomously pour into the 55 containers in the dataset with a very high
success rate. We also compare to a deep learning method. Results show that our
method achieves the same performance as the deep learning method on open
container classification and outperforms it on autonomous pouring. Moreover,
our method is fully explainable.
