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.

翻译：在人机协作的日常任务中，操作者需要简单的方法来编程新技能，而机器人则需具备适应环境变化的能力。视觉伺服与模仿学习的结合使我们能够实现友好型机器人接口的目标：（i）通过视觉感知适应环境，（ii）通过模仿先前演示避免显式编程。本研究旨在利用模仿学习改进视觉伺服范式，以解决移动目标追踪这一具体问题。特别地，我们证明了可以从数据中推断出实现追踪控制器所需的补偿项，从而避免显式实现估计器或观测器。通过机械臂仿真实验验证了该方法的有效性。