Imitation Learning-based Visual Servoing for Tracking Moving Objects

TL;DR

This paper presents a novel approach combining imitation learning and visual servoing to enable robots to adaptively track moving objects without explicit programming, validated through simulation experiments.

Contribution

It introduces a data-driven method to infer compensation terms for tracking, eliminating the need for explicit estimators or observers in visual servoing.

Findings

Successful simulation validation with robotic manipulators.

Effective adaptation to environmental changes in tracking tasks.

Avoidance of explicit estimator implementation in control.

Abstract

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.