# Imitation Learning-based Visual Servoing for Tracking Moving Objects

arXiv: https://arxiv.org/abs/2309.07729 · HTML: https://mlsanity.com/paper/2309.07729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 6 of 6)

- [Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning](https://mlsanity.com/paper/2204.02863/) (2022)
- [An Energy-based Approach to Ensure the Stability of Learned Dynamical Systems](https://mlsanity.com/paper/2003.11290/) (2020)
- [Learning Stable Dynamical Systems for Visual Servoing](https://mlsanity.com/paper/2204.05681/) (2022)
- [Visual Servoing with Geometrically Interpretable Neural Perception](https://mlsanity.com/paper/2210.10549/) (2022)
- [Incremental Skill Learning of Stable Dynamical Systems](https://mlsanity.com/paper/2003.11803/) (2020)
- [A memory of motion for visual predictive control tasks](https://mlsanity.com/paper/2001.11759/) (2020)

## Built on by (most cited first, 2 of 2)

- [Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation](https://mlsanity.com/paper/2406.09120/) (2024)
- [DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation](https://mlsanity.com/paper/2609.37348/) (2026)

## Abstract (arXiv)

In everyday life collaboration tasks between human operators and robots, the former necessitate simple ways for programming new skills, the latter have to show adaptive capabilities to cope with environmental changes. The joint use of visual servoing and imitation learning allows us to pursue the objective of realizing friendly robotic interfaces that (i) are able to adapt to the environment thanks to the use of visual perception and (ii) avoid explicit programming thanks to the emulation of previous demonstrations. This work aims to exploit imitation learning for the visual servoing paradigm to address the specific problem of tracking moving objects. In particular, we show that it is possible to infer from data the compensation term required for realizing the tracking controller, avoiding the explicit implementation of estimators or observers. The effectiveness of the proposed method has been validated through simulations with a robotic manipulator.