A machine learning-based motion training approach applied to multilegged and bipedal robots

Abstract

To promptly gain an understanding of disasters as they occur and to draft plans for search and rescue operations, various types of robots are used. Robots not only increase rescue efficiency but also reduce firefighter casualties. Therefore, this study investigated how bionic robots can be utilized to search destroyed and chaotic disaster sites as quickly as possible. However, manually adjusting the motion parameters of robots performing robot motions is extremely inefficient. To resolve this problem, this study used machine learning algorithms to allow robots to train themselves in the environment and autonomously determine their optimal motion parameters. However, many types of machine learning algorithms exist, each with their own strengths and weaknesses. Therefore, this study designed a series of experiments to investigate the features of each algorithm in optimizing the robots’ motions; subsequently, this study compared the strengths and weaknesses of each algorithm based on their performance. The results indicated that for both multipedal and bipedal robots, the use of machine learning to find the optimal motion parameters is both feasible and practical.