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用遗传规划控制仿人机器人

Controlling Humanoid Robots by Means of Genetic Programming
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)对机器人技术的现实应用,称为“进化机器人”。机器学习技术可以应用于机器人,以便在没有预先确定适当的动作的情况下完成任务。在这种情况下,机器人可以通过在重新环境中使用反复试验来获取适当的动作。 GP(遗传编程)可以直接生成控制机器人的程序,并且已经进行了许多研究表明.GA(遗传算法)结合神经网络(NN)也可以用来控制机器人。无论使用何种方法,评估真正的机器人需要大量的时间,部分原因在于其复杂的机械动作。此外,对于GP和GA中的许多人来说,评估必须重复几代。因此,在大多数研究中,学习是进行模拟,并且所获得的结果应用于真实的机器人。为了解决这些困难,我们提出了一种综合技术。遗传编程和强化学习(RL)使区域机器人能够在真实环境中调整其行为。 Ourtechnique不需要精确的模拟器,因为学习是通过真实的机器人实现的。此外,我们的技术使真正的机器人能够学习有效的行动。基于这种提出的技术,我们使用GP发展了常用程序,适用于各种类型的机器人。使用这个演化程序,我们在realrobot中执行强化学习。通过我们的方法,机器人可以适应自己的操作特性并学习有效的操作。通过使用真实的类人机器人进行实验来证明所提出的方法的有效性。
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: 进化计算; 机器学习技术; 遗传编程
课程来源: 视频讲座网
最后编审: 2019-03-24:cwx
阅读次数: 104