A developmental approach to robotic pointing via human–robot interaction

Chao, Fei; Wang, Zhengshuai; Shang, Changjing; Meng, Qinggang; Jiang, Min; Zhou, Changle; Shen, Qiang

doi:10.1016/j.ins.2014.03.104

Cited by 26 publications

(17 citation statements)

References 41 publications

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“…Specifically, in previously models the basis function layer neurons used radial, typically Gaussian, activation functions [4,9,13,14,17,18,20,23,24], and the parameters of these Gaussian activation functions (the centre and spread of each basis function neuron RF) were defined through some heuristic or optimization procedure. However, the PC/BC-DIM basis function network does not set the response profile of the basis function neurons through a pre-defined Gaussian activation function.…”

Section: Network Architecturementioning

confidence: 99%

“…If this action was unsuccessful then a basis function neuron was added. Therefore, with the PC/BC-DIM basis function network both learning and optimization processes were performed in one step instead of two separate learning phases as reported in previously published basis function models [4,9,13,14,17,18,20,23,24].…”

Section: Learning and Optimizationmentioning

confidence: 99%

“…Basis function networks are very popular in robotics for complex non-linear sensory-motor transformations [13][14][15][16][17][18][19][20][21][22][23]. For non-linear and complex sensory-motor transformations setting of number of basis function neurons, their receptive fields (RFs) sizes and peak locations is a non-trivial task which cannot be pre-defined or hand-crafted.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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A neural model for eye–head–arm coordination

Muhammad

Spratling

2017

Advanced Robotics

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The coordinated movement of the eyes, the head and the arm is an important ability in both animals and humanoid robots. To achieve this the brain and the robot control system need to be able to perform complex non-linear sensory-motor transformations in the forward and inverse directions between many degrees of freedom. In this article, we apply an omni-directional basis function neural network to this task. The proposed network can perform 3-D coordinated gaze shifts and 3-D arm reaching movements to a visual target. Particularly, it can perform direct sensory-motor transformations to shift gaze and to execute arm reach movements and can also perform inverse sensory-motor transformations in order to shift gaze to view the hand.

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Section: Network Architecturementioning

confidence: 99%

Section: Learning and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neural model for eye–head–arm coordination

Muhammad

Spratling

2017

Advanced Robotics

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“…Compared with traditional robots, grounded on conventional artificial intelligence, research on "developmental robotics" focuses on building autonomous learning abilities by following human developmental procedures [2]- [4]. Recent studies also indicate that human-like behavioral patterns reduce robotic learning complexities and increase learning speed [5], [6]. Therefore, this work focuses on applying human-like behavioral patterns and developmental learning methods to enhance the robot's 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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