  As autonomous robots interact and navigate around real-world environments
such as homes, it is useful to reliably identify and manipulate articulated
objects, such as doors and cabinets. Many prior works in object articulation
identification require manipulation of the object, either by the robot or a
human. While recent works have addressed predicting articulation types from
visual observations alone, they often assume prior knowledge of category-level
kinematic motion models or sequence of observations where the articulated parts
are moving according to their kinematic constraints. In this work, we propose
FormNet, a neural network that identifies the articulation mechanisms between
pairs of object parts from a single frame of an RGB-D image and segmentation
masks. The network is trained on 100k synthetic images of 149 articulated
objects from 6 categories. Synthetic images are rendered via a photorealistic
simulator with domain randomization. Our proposed model predicts motion
residual flows of object parts, and these flows are used to determine the
articulation type and parameters. The network achieves an articulation type
classification accuracy of 82.5% on novel object instances in trained
categories. Experiments also show how this method enables generalization to
novel categories and be applied to real-world images without fine-tuning.
