ロボティクス研究はコンピュータビジョン研究から何を学べるか
What can robotics research learn from computer vision research?
コンピュータビジョンの急速な進展は深層学習だけでなく厳格な制約下での評価と数値による比較という研究手法に起因すると論じ、ロボティクス研究への示唆を考察した論文。
著者: Peter Corke, Feras Dayoub, David Hall, John Skinner, Niko Sünderhauf
分類: cs.RO, cs.CV
原文アブストラクト
The computer vision and robotics research communities are each strong. However progress in computer vision has become turbo-charged in recent years due to big data, GPU computing, novel learning algorithms and a very effective research methodology. By comparison, progress in robotics seems slower. It is true that robotics came later to exploring the potential of learning -- the advantages over the well-established body of knowledge in dynamics, kinematics, planning and control is still being debated, although reinforcement learning seems to offer real potential. However, the rapid development of computer vision compared to robotics cannot be only attributed to the former's adoption of deep learning. In this paper, we argue that the gains in computer vision are due to research methodology -- evaluation under strict constraints versus experiments; bold numbers versus videos.
