# Visual Servoing with Geometrically Interpretable Neural Perception

arXiv: https://arxiv.org/abs/2210.10549 · HTML: https://mlsanity.com/paper/2210.10549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Imitation Learning-based Visual Servoing for Tracking Moving Objects](https://mlsanity.com/paper/2309.07729/) (2023)
- [Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation](https://mlsanity.com/paper/2406.09120/) (2024)
- [Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control](https://mlsanity.com/paper/2609.34018/) (2026)

## Abstract (arXiv)

An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.