A Developmental Approach to Mobile Robotic Reaching

Zhu, Zuyuan; Chao, Fei; Zhang, Xin; Jiang, Min; Zhou, Changle

doi:10.1007/978-3-319-22876-1_25

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches enable robots to gradually develop multiple basic skills and thus learn to handle complex tasks (Berthouze and Lungarella, 2004 ; Jiang et al, 2014 ). In other words, the learning target of a complex set of skills are divided into the learning of a number of stage targets (Wang et al, 2013 ; Zhu et al, 2015 ), and the robot achieves the ultimate learning goal by completing a series of sub learning goals. This method reduces the difficulty for the robot to learn new skills (Shaw et al, 2014 ), and gives the robot the ability to accumulate learning, where the basic skills learned during the development process are reserved so as to arrive at the final skill (Lee et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

A Developmental Learning Approach of Mobile Manipulator via Playing

Zhou

Chao

et al. 2017

Front. Neurorobot.

Self Cite

View full text Add to dashboard Cite

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games.

show abstract