With the rapid growth of research in AI and robotics now producing over 10,000 papers annually it has become increasingly difficult for researchers to stay up to date. Fast evolving trends, the rise of interdisciplinary work, and the need to explore domains beyond one's expertise all contribute to this challenge. To address these issues, we propose a generalizable pipeline capable of systematically analyzing any research area: identifying emerging trends, uncovering cross domain opportunities, and offering concrete starting points for new inquiry. In this work, we present Real Deep Research (RDR) a comprehensive framework applied to the domains of AI and robotics, with a particular focus on foundation models and robotics advancements. We also briefly extend our analysis to other areas of science. The main paper details the construction of the RDR pipeline, while the appendix provides extensive results across each analyzed topic. We hope this work sheds light for researchers working in the field of AI and beyond.
翻译:随着人工智能与机器人学领域研究规模的迅速扩张,当前每年产出论文已超过10,000篇,研究人员保持对前沿进展的跟进日益困难。快速演变的研究趋势、跨学科工作的兴起,以及探索自身专业外领域的需求,共同加剧了这一挑战。为应对这些问题,我们提出一种可泛化的分析流程,能够系统性地剖析任何研究领域:识别新兴趋势、发掘跨领域机遇,并为新的研究方向提供具体的切入点。本研究提出了“真实深度研究”(Real Deep Research, RDR)这一综合性框架,并将其应用于人工智能与机器人学领域,特别聚焦于基础模型与机器人技术的进展。我们亦将分析简要拓展至其他科学领域。正文详细阐述了RDR流程的构建方法,附录则提供了各分析主题的详尽结果。我们希望这项工作能为人工智能及相关领域的研究者提供启示。