  Deformable object manipulation requires computationally efficient
representations that are compatible with robotic sensing modalities. In this
paper, we present VIRDO:an implicit, multi-modal, and continuous representation
for deformable-elastic objects. VIRDO operates directly on visual (point cloud)
and tactile (reaction forces) modalities and learns rich latent embeddings of
contact locations and forces to predict object deformations subject to external
contacts.Here, we demonstrate VIRDOs ability to: i) produce high-fidelity
cross-modal reconstructions with dense unsupervised correspondences, ii)
generalize to unseen contact formations,and iii) state-estimation with partial
visio-tactile feedback
