  Robots need both visual and contact sensing to effectively estimate the state
of their environment. Camera RGBD data provides rich information of the objects
surrounding the robot, and shape priors can help correct noise and fill in gaps
and occluded regions. However, when the robot senses unexpected contact, the
estimate should be updated to explain the contact. To address this need, we
propose CLASP: Constrained Latent Shape Projection. This approach consists of a
shape completion network that generates a prior from RGBD data and a procedure
to generate shapes consistent with both the network prior and robot contact
observations. We find CLASP consistently decreases the Chamfer Distance between
the predicted and ground truth scenes, while other approaches do not benefit
from contact information.
