Extended QDSEGA for controlling real robots - acquisition of locomotion patterns for snake-like robot
Extended QDSEGA for controlling real robots - acquisition of locomotion patterns for snake-like robot
复制标题
用于控制真实机器人的扩展 QDSEGA - 获取蛇形机器人的运动模式
DOI：
10.1109/robot.2003.1241690
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
F. Matsuno
中科院分区：
文献类型：
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作者：
Kazuyuki Ito;T. Kamegawa;F. Matsuno
强化学习是一种非常有效的机器人学习方法。这是因为它不需要先验知识，具有更强的反应和适应行为的能力。在以前的工作中，我们提出了一种新的强化学习算法：基于遗传算法的动态构造探索空间的Q学习算法(QDSEGA)。它是为具有大动作状态空间的复杂系统而设计的，例如具有许多冗余自由度的机器人。然而，QDSEGA的应用仅限于静态系统。蛇形机器人具有多个冗馀自由度，系统的动力学特性对其运动任务的完成至关重要。因此，通常的强化学习的应用是非常困难的。在本文中，我们扩展了QDSEGA的分层结构，使其有可能应用于具有复杂性和动力学的真实机器人。将其应用于蛇形机器人运动模式的获取，并通过仿真和实验验证了扩展分层结构QDSEGA的有效性和有效性。
Reinforcement learning is very effective for robot learning. It is because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: "Q-learning with dynamic structuring of exploration space based on genetic algorithm (QDSEGA)". It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems. A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. So application of usual reinforcement learning is very difficult. In this paper, we extend layered structure of QDSEGA so that it becomes possible to apply it to real robots that have complexities and dynamics. We apply it to acquisition of locomotion pattern of the snake-like robot and demonstrate the effectiveness and the validity of QDSEGA with the extended layered structure by simulation and experiment.