论文标题
从模拟到现实世界的学习机器
Learning Machines from Simulation to Real World
论文作者
论文摘要
Learning Machines正在开发一个灵活的，跨行业，高级分析平台，该平台针对隐形阶段，以有限数量的特定垂直应用。在本文中，我们旨在集成通用机器系统，以学习从模拟到现实世界的任务变体。在这样的机器系统中，它涉及实时机器人视觉，传感器融合和学习算法（增强学习）。为此，我们在三个基本任务上演示了通用机器系统，包括避免障碍物，觅食和捕食者 - 捕食机器人。所提出的解决方案是在Robobo机器人上使用移动设备（带相机智能手机）作为接口和内置红外（IR）传感器实现的。该代理在虚拟环境中接受培训。为了评估其性能，在虚拟环境中测试了学习的代理，并在真实的环境中重现相同的结果。结果表明，在未知环境中，可以可靠地使用增强学习算法。
Learning Machines is developing a flexible, cross-industry, advanced analytics platform, targeted during stealth-stage at a limited number of specific vertical applications. In this paper, we aim to integrate a general machine system to learn a variant of tasks from simulation to real world. In such a machine system, it involves real-time robot vision, sensor fusion, and learning algorithms (reinforcement learning). To this end, we demonstrate the general machine system on three fundamental tasks including obstacle avoidance, foraging, and predator-prey robot. The proposed solutions are implemented on Robobo robots with mobile device (smartphone with camera) as interface and built-in infrared (IR) sensors. The agent is trained in a virtual environment. In order to assess its performance, the learned agent is tested in the virtual environment and reproduce the same results in a real environment. The results show that the reinforcement learning algorithm can be reliably used for a variety of tasks in unknown environments.
