NDLI: A sensorimotor approach for self-learning of hand-eye coordination

Content Provider	IEEE Xplore Digital Library
Author	Ghadirzadeh, A. Maki, A. Bjorkman, M.
Copyright Year	2015
Description	Author affiliation: Comput. Vision & Active Perception Lab. (CVAP), KTH R. Inst. of Technol., Stockholm, Sweden (Ghadirzadeh, A.; Maki, A.; Bjorkman, M.)
Abstract	This paper presents a sensorimotor contingencies (SMC) based method to fully autonomously learn to perform hand-eye coordination. We divide the task into two visuomotor subtasks, visual fixation and reaching, and implement these on a PR2 robot assuming no prior information on its kinematic model. Our contributions are three-fold: i) grounding a robot in the environment by exploiting SMCs in the action planning system, which eliminates the need for prior knowledge of the kinematic or dynamic models of the robot; ii) using a forward model to search for proper actions to solve the task by minimizing a cost function, instead of training a separate inverse model, to speed up training; iii) encoding 3D spatial positions of a target object based on the robot's joint positions, thus avoiding calibration with respect to an external coordinate system. The method is capable of learning the task of hand-eye coordination from scratch by less than 20 sensory-motor pairs that are iteratively generated at real-time speed. In order to examine the robustness of the method while dealing with nonlinear image distortions, we apply a so-called retinal mapping image deformation to the input images. Experimental results show the successfulness of the method even under considerable image deformations.
Starting Page	4969
Ending Page	4975
File Size	1123472
Page Count	7
File Format	PDF
e-ISBN	9781479999941
DOI	10.1109/IROS.2015.7354076
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-28
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Predictive models Robot kinematics Training Mathematical model Cost function
Content Type	Text
Resource Type	Article

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