An Infant Development-inspired Approach to Robot Hand-eye Coordination

Abstract

This paper presents a novel developmental learning approach for hand-eye coordination in an autonomous robotic system. Robotic hand-eye coordination plays an important role in dealing with realtime environments. Under the approach, infant developmental patterns are introduced to build our robot's learning system. The method works by first constructing a brain-like computational structure to control the robot, and then by using infant behavioural patterns to build a hand-eye coordination learning algorithm. This work is supported by an experimental evaluation, which shows that the control system is implemented simply, and that the learning approach provides fast and incremental learning of behavioural competence.

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

“…Existing research focuses on applying various solutions to solve the non-linear mapping problem for the hand-eye coordination. Our previous work [17] developed a two-dimensional mapping mechanism, which can be expanded to have a certain level of reverse mapping ability. Similarity, Huelse et al's research also developed a common spatial reference frame, they claimed the frame supports transformation from the reaching space to the gaze space [28].…”

“…The visual system, inspired by our previous work [34], produces an initial processing on the images received from the image capture system. This retina-like visual system is widely used in many developmental robotic models [17], [45], [46].…”

“…How to establish the stimulus transformation from robotic visual space to robotic actuator space is the most principle part of robotic hand-eye coordination [17]. Usually, a robot firstly tries to detect any salient objects in images, which are captured by the robot's visual sensor.…”

“…The mathematical approach is especially suitable for static or industrial environments (see [10]); however, the learning approach brings more self-adaptive properties to robots. Furthermore, because robotic hand-eye coordination involves the most basic application of robotic internal representation [11]- [13], developing human-like reaching and grasping skills is regarded as an important stage to establish higher robotic cognitive processes [14]- [17].…”

“…Usually, motor-motor coordination performs such a mapping mechanism [17]. In addition, much research suggests skills and knowledge be transferred from human beings to robots so as to reduce learning complexity [18]- [22].…”