# Transferring Grasping Skills to Novel Instances by Latent Space Non-Rigid Registration

arXiv: https://arxiv.org/abs/1809.05353 · HTML: https://mlsanity.com/paper/1809.05353/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [One-shot Imitation Learning via Interaction Warping](https://mlsanity.com/paper/2306.12392/) (2023)
- [FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation](https://mlsanity.com/paper/2502.11744/) (2025)
- [Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement](https://mlsanity.com/paper/2403.14000/) (2024)
- [Learning Generalizable Tool Use with Non-rigid Grasp-pose Registration](https://mlsanity.com/paper/2307.16499/) (2023)

## Abstract (arXiv)

Robots acting in open environments need to be able to handle novel objects. Based on the observation that objects within a category are often similar in their shapes and usage, we propose an approach for transferring grasping skills from known instances to novel instances of an object category. Correspondences between the instances are established by means of a non-rigid registration method that combines the Coherent Point Drift approach with subspace methods. The known object instances are modeled using a canonical shape and a transformation which deforms it to match the instance shape. The principle axes of variation of these deformations define a low-dimensional latent space. New instances can be generated through interpolation and extrapolation in this shape space. For inferring the shape parameters of an unknown instance, an energy function expressed in terms of the latent variables is minimized. Due to the class-level knowledge of the object, our method is able to complete novel shapes from partial views. Control poses for generating grasping motions are transferred efficiently to novel instances by the estimated non-rigid transformation.