1 research outputs found
Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body
For robots to exhibit a high level of intelligence in the real world, they
must be able to assess objects for which they have no prior knowledge.
Therefore, it is crucial for robots to perceive object affordances by reasoning
about physical interactions with the object. In this paper, we propose a novel
method to provide robots with an ability to imagine object affordances using
physical simulations. The class of chair is chosen here as an initial category
of objects to illustrate a more general paradigm. In our method, the robot
"imagines" the affordance of an arbitrarily oriented object as a chair by
simulating a physical sitting interaction between an articulated human body and
the object. This object affordance reasoning is used as a cue for object
classification (chair vs non-chair). Moreover, if an object is classified as a
chair, the affordance reasoning can also predict the upright pose of the object
which allows the sitting interaction to take place. We call this type of poses
the functional pose. We demonstrate our method in chair classification on
synthetic 3D CAD models. Although our method uses only 30 models for training,
it outperforms appearance-based deep learning methods, which require a large
amount of training data, when the upright orientation is not assumed to be
known a priori. In addition, we showcase that the functional pose predictions
of our method align well with human judgments on both synthetic models and real
objects scanned by a depth camera.Comment: 7 pages, 6 figures. Accepted to ICRA202