模倣学習に基づく移動物体追跡のためのビジュアルサーボ
Imitation Learning-based Visual Servoing for Tracking Moving Objects
ビジュアルサーボと模倣学習を組み合わせ、推定器やオブザーバを明示的に実装せずにデータから追従制御の補償項を学習し、移動物体追跡を実現する手法を提案した。
著者: Rocco Felici, Matteo Saveriano, Loris Roveda, Antonio Paolillo
分類: cs.RO
原文アブストラクト
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.
