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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Learning how to grasp based on neural network retraining

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Author(s):
Pedro, Leonardo M. [1] ; Belini, Valdinei L. [1] ; Caurin, Glauco A. P. [2]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Carlos, Ctr Exact Sci & Technol, Sao Paulo - Brazil
[2] Univ Sao Paulo, Engn Sch Sao Carlos, Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: ADVANCED ROBOTICS; v. 27, n. 10, p. 785-797, JUL 1 2013.
Web of Science Citations: 5
Abstract

Humans have an incredible capacity to manipulate objects using dextrous hands. A large number of studies indicate that robot learning by demonstration is a promising strategy to improve robotic manipulation and grasping performance. Concerning this subject we can ask: How does a robot learn how to grasp? This work presents a method that allows a robot to learn new grasps. The method is based on neural network retraining. With this approach we aim to enable a robot to learn new grasps through a supervisor. The proposed method can be applied for 2D and 3D cases. Extensive object databases were generated to evaluate the method performance in both 2D and 3D cases. A total of 8100 abstract shapes were generated for 2D cases and 11700 abstract shapes for 3D cases. Simulation results with a computational supervisor show that a robotic system can learn new grasps and improve its performance through the proposed HRH (Hopfield-RBF-Hopfield) grasp learning approach. (AU)