Hive Mind: A Framework for Problem Solving in Multi-Agent Systems

Hive Mind：多智能体系统中问题解决的框架

• 批准号: 2172887
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2172887
• 关键词: Hive; Mind; Framework; Problem; Solving

This project focuses on developing artificial intelligence algorithms for learning physics models from observations of both computational simulation and real-world data. The final aim is for a robot to use these models to perform basic tasks such as grasping and picking an object with a manipulator. The research hypothesis is that by allowing the robot to learn to predict the effect of its behaviour, we can achieve better training and runtime performance when executing tasks.The research will be carried out, in a first stage, by developing reinforcement learning techniques (i.e. training a robot to achieve a goal through trial and error) in a graphics and physics simulation engine. Data captured with real depth camera sensors will be fed back into the simulation to refine environment variables, potentially leading to better behaviour models. While object grasping has been subject of significant research over the past years with already successful applications in industry, the incorporation of physics models will expand these techniques to work with a wider range of objects, taking into account additional features such as perceived weight and friction.

该项目专注于开发人工智能算法，用于通过计算模拟和现实数据的观察来学习物理模型。最终目标是让机器人使用这些模型来执行基本任务，例如使用机械手抓取和拾取物体。研究假设是，通过让机器人学习预测其行为的影响，我们可以在执行任务时获得更好的训练和运行时性能。该研究将在第一阶段通过在图形和物理模拟引擎中开发强化学习技术（即训练机器人通过反复试验来实现目标）来进行。使用真实深度相机传感器捕获的数据将反馈到模拟中以细化环境变量，从而可能产生更好的行为模型。虽然物体抓取在过去几年中一直是重要研究的主题，并已在工业中成功应用，但物理模型的结合将扩展这些技术以处理更广泛的物体，同时考虑到感知重量和摩擦等其他特征。