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
@article{arxiv.2202.00868,
title = {VIRDO: Visio-tactile Implicit Representations of Deformable Objects},
author = {Youngsun Wi and Pete Florence and Andy Zeng and Nima Fazeli},
journal= {arXiv preprint arXiv:2202.00868},
year = {2022}
}