ドメインランダム化によるシミュレーションから実世界への深層ニューラルネットワークの転移
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
※ 解説はAIが生成。被引用数はOpenAlex由来（取得できた論文のみ表示）。詳細は原論文をご確認ください。
シミュレータの描画をランダム化するドメインランダム化により、実画像で事前学習せずともシミュレーションのみで学習した物体検出器が実世界で1.5cm精度を達成し、雑然とした環境での把持に成功した。
著者: Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel
分類: cs.RO, cs.LG
原文アブストラクト
Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-world object detector that is accurate to $1.5$cm and robust to distractors and partial occlusions using only data from a simulator with non-realistic random textures. To demonstrate the capabilities of our detectors, we show they can be used to perform grasping in a cluttered environment. To our knowledge, this is the first successful transfer of a deep neural network trained only on simulated RGB images (without pre-training on real images) to the real world for the purpose of robotic control.
