実シーンと合成データ生成の効率的な連携：AIベースの認知ロボティクスとコンピュータビジョン応用に向けて
Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications
AI視覚モデルの訓練データ生成において、シミュレーションと実世界のドメインギャップを埋める手法について議論し、現在進行中の研究を紹介する論文。
著者: Paul Koch, Vivek Chavan, André Sers, Adem Karakurt, Paul Hofmann, Mohamad Zaher Ziadeh, Jörg Krüger
分類: cs.RO, cs.CV
原文アブストラクト
AI vision models are a driving factor for the potential use case scenarios of cognitive robotics within in the industry and household applications. A large array of methods from semantic environment analysis towards 6D and grasping pose estimation have been proposed based on the latest AI achievements. However, such advancements require further strong and efficient methods w.r.t. training data and AI-architectures, which are capable in synergy to tackle current challenges, precision limits, and scalability beyond domain gaps. In this paper, we discuss these current limits and trends in the related state-of-the-art which are challenging those. Further we discuss our current work in progress on bridging the domain gap between simulations and real world applications by linking those in the training data generation.