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

该项目的重点是开发人工智能算法，用于从计算模拟和真实世界数据的观察中学习物理模型。最终的目标是机器人使用这些模型来执行基本的任务，例如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实现更好的训练和运行时性能。研究将在第一阶段通过在图形和物理仿真引擎中开发强化学习技术（即通过试验和错误来训练机器人实现目标）来进行。使用真实的深度相机传感器捕获的数据将被反馈到模拟中，以优化环境变量，从而可能导致更好的行为模型。虽然物体抓取在过去几年中一直是重要的研究课题，并且已经在工业中获得了成功的应用，但物理模型的引入将扩展这些技术，以处理更广泛的物体，同时考虑到其他特征，如感知重量和摩擦力。

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.