论文标题
机器人研究可以从计算机视觉研究中学到什么？
What can robotics research learn from computer vision research?
论文作者
论文摘要
计算机视觉和机器人研究社区每个人都很强。但是，由于大数据，GPU计算，新颖的学习算法和一种非常有效的研究方法，近年来，计算机视觉的进展已成为涡轮增压。相比之下，机器人技术的进度似乎较慢。的确，机器人技术后来探索学习的潜力 - 尽管强化学习似乎具有真正的潜力，但仍在争论动态，运动学，计划和控制方面的知识，运动和控制方面的优势。但是，与机器人技术相比，计算机视觉的快速发展不能仅归因于前者的深度学习。在本文中，我们认为计算机视觉的收益是由于研究方法 - 在严格的限制下与实验的评估；大胆的数字与视频。
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.
