Learning Robotic Hand-eye Coordination Through a Developmental Constraint Driven Approach

Abstract

Abstract:The skill of robotic hand-eye coordination not only helps robots to deal with real time environment, but also affects the fundamental framework of robotic cognition. A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination. However, several important features within infant developmental procedure have not been introduced into such approaches. This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants. The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination, and adopts a developmental mechanism from psychology to drive the robot. The entire learning procedure is driven by developmental constraint: The robot starts to act under fully constrained conditions, when the robot learning system becomes stable, a new constraint is assigned to the robot. After that, the robot needs to act with this new condition again. When all the contained conditions have been overcome, the robot is able to obtain hand-eye coordination ability. The work is supported by experimental evaluation, which shows that the new approach is able to drive the robot to learn autonomously, and make the robot also exhibit developmental progress similar to human infants.

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

“…Because artificial neural networks contain excellent non-linear approximation ability, the robotic hand-eye mappings used various types of artificial neural networks [37]. For example, a double neural network structure mimicking the working loops between the basal ganglia and cerebra was adopted to create a robot system, which can handle reaching movements by using long and short movements [9].…”

“…Robotic hand-eye coordination, or reaching ability, regarded as a basic cooperation of robotic eyes and hands/arms, is implemented by two radically different methods: (1) the mathematical approach, which employs forward or inverse kinematics [8]; and (2) the learning approach [9], which uses mainly artificial neural networks. The mathematical approach is especially suitable for static or industrial environments (see [10]); however, the learning approach brings more self-adaptive properties to robots.…”

“…In addition, much research suggests skills and knowledge be transferred from human beings to robots so as to reduce learning complexity [18]- [22]. Several other approaches have focused on applying artificial neural networks to build the robotic learning system [9], [23]. Meng et al [24], [25] proposed an improved minimal resource allocation network model to build a robot mapping system to bring more developmental features into robots.…”

A neural model for eye–head–arm coordination

“…All above mentioned basis function models were trained using two step complex training phases: learning of number of basis function neurons their RFs sizes and peak locations; learning of connection weights between the basis function neurons and the output neurons. For number of basis function neurons, RFs sizes and locations optimization: [23] used orthogonal least square algorithm, [18][19][20] used simplified node-decoupled extended Kalman filter algorithm, in [13,14,17,24] basis function units with fixed RFs size and defined locations were used. To learn the network connection weights: [17] used least-mean-square (LMS) gradient descent learning technique, in [23] linear least square (LLS) algorithm was used, in [18][19][20] simplified node-decouple extended Kalman filter (SDEKF) algorithm was employed, [14] used delta rule gradient descent technique, [13] used recursive least square (RLS) algorithm and in [24] extended Kalman filter was used.…”

“…Moreover, little work has been done on 3-D eye-head-arm coordination using basis function networks. Some methods have applied basis function networks to perform only the direct visuo-motor transformation [18,23,24]. Other work [13,14,17] has used basis function networks to implement bi-directional visuo-motor trans-formations, however to do so separate networks for direct and inverse transformations were required.…”