  This paper considers the task of locating articulated poses of multiple
robots in images. Our approach simultaneously infers the number of robots in a
scene, identifies joint locations and estimates sparse depth maps around joint
locations. The proposed method applies staged convolutional feature detectors
to 2D image inputs and computes robot instance masks using a recurrent network
architecture. In addition, regression maps of most likely joint locations in
pixel coordinates together with depth information are computed. Compositing 3D
robot joint kinematics is accomplished by applying masks to joint readout maps.
Our end-to-end formulation is in contrast to previous work in which the
composition of robot joints into kinematics is performed in a separate
post-processing step. Despite the fact that our models are trained on
artificial data, we demonstrate generalizability to real world images.
