用合成数据连接真实场景，提升机器人视觉模型的泛化能力
Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications
- 通过合成数据与真实场景联动生成训练数据
- 缓解仿真与现实间的域差异问题
- 适合做机器人视觉与工业应用的研究者
AI视觉模型是推动认知机器人在工业和家庭场景中应用的关键因素。基于最新AI成果，已提出多种方法用于语义环境分析、6D姿态估计及抓取位姿估计。然而，这些进展仍需更强且更高效的训练数据与AI架构协同，以应对当前精度限制、可扩展性挑战以及跨领域差距问题。本文讨论了相关领域的现有局限与趋势，并介绍了我们正在进行的工作：通过在训练数据生成中链接仿真与真实世界，弥合二者之间的域差距。
原文摘要 · Abstract (English)
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.
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