图 书 馆
基于遗传规划的仿人机器人控制Controlling Humanoid Robots by Means of Genetic Programming |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_iba_chrmgp/ |
| 主讲教师: | Hitoshi Iba |
| 开课单位: | 东京大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  我们展示了EC（进化计算）在机器人技术中的实际应用，这被称为“进化机器人”。如果未预先确定适当的动作，则可以将机器学习技术应用于机器人以完成任务。在这种情况下，机器人可以通过在真实环境中使用试错法来学习适当的动作。 GP（遗传编程）可以生成直接控制机器人的程序，并且已经进行了许多研究。 GA（遗传算法）与神经网络（NN）的组合也可用于控制机器人。无论使用哪种方法，对真实机器人的评估都需要大量时间，部分原因是它们具有复杂的机械作用。此外，必须对GP和GA中的许多人进行几代人的评估。因此，在大多数研究中，学习都是在仿真中进行的，并将获得的结果应用于真实的机器人。为了解决这些困难，我们提出了一种遗传编程和强化学习（RL）的集成技术，以使真正的机器人能够在现实环境中适应其动作。我们的技术不需要精确的模拟器，因为学习是通过真实的机器人完成的。此外，我们的技术使真正的机器人可以学习有效的动作。基于此提议的技术，我们使用GP开发了通用程序，这些程序适用于各种类型的机器人。使用这个经过改进的程序，我们可以在真实的机器人中执行强化学习。使用我们的方法，机器人可以适应其自身的操作特性并学习有效的措施。通过使用真实的人形机器人进行实验，证明了该方法的有效性。 |
| 课程简介: | We show the real-world applications of EC (evolutionary computation) to robotics, which is called "evolutionary robotics". Machine Learning techniques can be applied to a robot in order to achieve a task for it if the appropriate actions are not predetermined. In such a situation, the robot can learn the appropriate actions by using trial-and-error in a real environment. GP (Genetic Programming) can generate programs to control a robot directly, and many studies have been done showing this. GA (Genetic Algorithms) in combination with neural networks (NN) can also be used to control robots. Regardless of the method used, the evaluation of real robots requires a significant amount of time partly due to their complex mechanical actions. Moreover, evaluations have to be repeated over several generations for many individuals in both GP and GA. Therefore, in most studies, the learning is conducted in simulation, and the acquired results are applied to real robots. To solve these difficulties, we propose an integrated technique of genetic programming and reinforcement learning (RL) to enable a real robot to adapt its actions in a real environment. Our technique does not require a precise simulator because learning is achieved through the real robot. In addition, our technique makes it possible for real robots to learn effective actions. Based on this proposed technique, we evolve common programs using GP, which are applicable to various types of robots. Using this evolved program, we execute reinforcement learning in a real robot. With our method, the robot can adapt to its own operational characteristics and learn effective actions. The effectiveness of our proposed approach is demonstrated by performing experiments with real humanoid robots. |
| 关 键 词: | EC; 进化计算; GP; 遗传编程; GA; 遗传算法; NM; 神经网络; RL; 强化学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-04-10：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 37 |