自主具身智能体：当机器人学遇上深度学习推理
Autonomous Embodied Agents: When Robotics Meets Deep Learning Reasoning
AI总结：
本论文探讨了Embodied AI领域自主具身智能体的完整创建过程，涵盖从概念、模拟训练到真实部署，并详细分析了相关技术的实现方法与实验研究，以促进该领域的未来发展。
摘要：
可用计算能力的提升和深度学习革命使得探索人工智能研究中的新主题和前沿成为可能。一个名为Embodied Artificial Intelligence（具身人工智能）的新领域位于计算机视觉、机器人学和决策制定的交叉点，在过去几年中日益重要，因为它旨在促进智能自主机器人的发展及其在社会中的部署。最近，大量用于照片级逼真机器人模拟的3D模型的可用性，使得基于学习的智能体能够在数百万帧上进行更快、更安全的训练，并在将模型部署到真实机器人平台之前，对其行为进行仔细评估。这些智能体旨在可能在未知环境中执行特定任务。为此，在模拟训练期间，智能体学习与周围环境进行持续交互，例如从环境中收集信息、编码并提取对任务有用的线索，以及执行面向最终目标的行动；智能体的每一个行动都会影响这些交互。本论文遵循了室内环境具身智能体从概念到实现和部署的完整创建过程。我们旨在为Embodied AI和自主智能体的研究做出贡献，以促进该领域的未来工作。我们详细分析了实现智能具身智能体背后的过程，包括对文献中当前最新技术的全面描述、所提方法的技术解释，以及在相关机器人任务上的精确实验研究。
英文摘要：
The increase in available computing power and the Deep Learning revolution have allowed the exploration of new topics and frontiers in Artificial Intelligence research. A new field called Embodied Artificial Intelligence, which places at the intersection of Computer Vision, Robotics, and Decision Making, has been gaining importance during the last few years, as it aims to foster the development of smart autonomous robots and their deployment in society. The recent availability of large collections of 3D models for photorealistic robotic simulation has allowed faster and safe training of learning-based agents for millions of frames and a careful evaluation of their behavior before deploying the models on real robotic platforms. These intelligent agents are intended to perform a certain task in a possibly unknown environment. To this end, during the training in simulation, the agents learn to perform continuous interactions with the surroundings, such as gathering information from the environment, encoding and extracting useful cues for the task, and performing actions towards the final goal; where every action of the agent influences the interactions. This dissertation follows the complete creation process of embodied agents for indoor environments, from their concept to their implementation and deployment. We aim to contribute to research in Embodied AI and autonomous agents, in order to foster future work in this field. We present a detailed analysis of the procedure behind implementing an intelligent embodied agent, comprehending a thorough description of the current state-of-the-art in literature, technical explanations of the proposed methods, and accurate experimental studies on relevant robotic tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
