# Vision-based Manipulation of Deformable and Rigid Objects Using Subspace Projections of 2D Contours

arXiv: https://arxiv.org/abs/2006.09023 · HTML: https://mlsanity.com/paper/2006.09023/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds](https://mlsanity.com/paper/2110.04685/) (2021)
- [DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects](https://mlsanity.com/paper/2305.04449/) (2023)
- [DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation](https://mlsanity.com/paper/2309.14463/) (2023)
- [Robotic Dough Shaping](https://mlsanity.com/paper/2208.00386/) (2022)
- [DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation](https://mlsanity.com/paper/2506.18779/) (2025)

## Abstract (arXiv)

This paper proposes a unified vision-based manipulation framework using image contours of deformable/rigid objects. Instead of using human-defined cues, the robot automatically learns the features from processed vision data. Our method simultaneously generates -- from the same data -- both, visual features and the interaction matrix that relates them to the robot control inputs. Extraction of the feature vector and control commands is done online and adaptively, with little data for initialization. The method allows the robot to manipulate an object without knowing whether it is rigid or deformable. To validate our approach, we conduct numerical simulations and experiments with both deformable and rigid objects.