ヒューリスティックとデータ駆動型アクティブビジョンによる新規物体の把持合成
Grasp Synthesis for Novel Objects Using Heuristic-based and Data-driven Active Vision Methods
腕搭載深度カメラの視点最適化により把持性能を高めるヒューリスティック・データ駆動型アクティブビジョン手法を提案し、シミュレーションと実機で評価した研究。
著者: Sabhari Natarajan, Galen Brown, Berk Calli
分類: cs.RO
原文アブストラクト
In this work, we present several heuristic-based and data-driven active vision strategies for viewpoint optimization of an arm-mounted depth camera for the purpose of aiding robotic grasping. These strategies aim to efficiently collect data to boost the performance of an underlying grasp synthesis algorithm. We created an open-source benchmarking platform in simulation (https://github.com/galenbr/2021ActiveVision), and provide an extensive study for assessing the performance of the proposed methods as well as comparing them against various baseline strategies. We also provide an experimental study with a real-world setup by utilizing an existing grasping planning benchmark in the literature. With these analyses, we were able to quantitatively demonstrate the versatility of heuristic methods that prioritize certain types of exploration, and qualitatively show their robustness to both novel objects and the transition from simulation to the real world. We identified scenarios in which our methods did not perform well and scenarios that are objectively difficult, and present a discussion on which avenues for future research show promise.
