任意の目標姿勢に対応するハイパーネットワークベースのエンドツーエンド視覚サーボ
A Hyper-network Based End-to-end Visual Servoing with Arbitrary Desired Poses
ハイパーネットワークを用いて任意の目標姿勢への6自由度視覚サーボを実現する微分可能なニューラルコントローラを提案し、3段階の学習手順で実世界物体への適用を可能にした。
著者: Hongxiang Yu, Anzhe Chen, Kechun Xu, Zhongxiang Zhou, Wei Jing, Yue Wang, Rong Xiong
分類: cs.RO
原文アブストラクト
Recently, several works achieve end-to-end visual servoing (VS) for robotic manipulation by replacing traditional controller with differentiable neural networks, but lose the ability to servo arbitrary desired poses. This letter proposes a differentiable architecture for arbitrary pose servoing: a hyper-network based neural controller (HPN-NC). To achieve this, HPN-NC consists of a hyper net and a low-level controller, where the hyper net learns to generate the parameters of the low-level controller and the controller uses the 2D keypoints error for control like traditional image-based visual servoing (IBVS). HPN-NC can complete 6 degree of freedom visual servoing with large initial offset. Taking advantage of the fully differentiable nature of HPN-NC, we provide a three-stage training procedure to servo real world objects. With self-supervised end-to-end training, the performance of the integrated model can be further improved in unseen scenes and the amount of manual annotations can be significantly reduced.
