爬行
机器人
计算机科学
蠕动
运动(音乐)
人工智能
功能(生物学)
强化学习
计算机视觉
物理
解剖
声学
医学
进化生物学
生物
作者
Norihiko Saga,Satoshi Tesen,Toshiyuki Sato,Jun‐ya Nagase
标识
DOI:10.1177/1729881416657740
摘要
In recent years, attention has been increasingly devoted to the development of rescue robots that can protect humans from the inherent risks of rescue work. Particularly, anticipated is the development of a robot that can move deeply through small spaces. We have devoted our attention to peristalsis, the movement mechanism used by earthworms. A reinforcement learning technique used for the derivation of the robot movement pattern, Q-learning, was used to develop a three-segmented peristaltic crawling robot with a motor drive. Characteristically, peristalsis can provide movement capability if at least three segments work, even if a segmented part does not function. Therefore, we had intended to derive the movement pattern of many-segmented peristaltic crawling robots using Q-learning. However, because of the necessary increase in calculations, in the case of many segments, Q-learning cannot be used because of insufficient memory. Therefore, we devoted our attention to a learning method called Actor–Critic, which can be implemented with low memory. Because Actor-Critic methods are TD methods that have a separate memory structure to explicitly represent the policy independent of the value function. Using it, we examined the movement patterns of six-segmented peristaltic crawling robots.
科研通智能强力驱动
Strongly Powered by AbleSci AI