A human-like learning approach to developmental robotic reaching

Wang, Zhengshuai; Chao, Fei; Lin, Haixiong; Jiang, Min; Zhou, Changle

doi:10.1109/robio.2013.6739522

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In addition, a retina-like visual mechanism and an incremental computational learning system are used to support the robotic hand-eye coordination ability. The enhanced robotic hand-eye coordination system extends the original idea [31] that works exclusively with the "Stopto-Fixate" pattern and bi-directional delivery channels, thus allowing an enhanced robotic hand-eye coordination ability.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Chao

Zhu

Lin

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method: Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: "Stop-to-Fixate", is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot's learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object.Index Terms-Robotic hand-eye coordination, sensory motor mapping, human-like behavioral pattern, constructive neural network.

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Section: Introductionmentioning

confidence: 99%

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Chao

Zhu

Lin

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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“…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.

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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.

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A human-like learning approach to developmental robotic reaching

Cited by 2 publications

References 18 publications

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

A Developmental Learning Approach of Mobile Manipulator via Playing

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