Bionics solution to learn the arm reaching with collision avoidance

Abstract

This article presents a learning model that simulates the control of an anthropomorphic arm kinematics motion. The objective is to reach and grasp a static prototypic object placed behind different kinds of obstacle in size and position. The network, composed of two generic neural network modules, learns to combine multi-modal arm-related information (trajectory parameters) as well as obstacle-related information (obstacle size and location). Our simulation was based on the notion of Via Point, which postulates that the motion planning that is divided into specific successive position of the arm. In order to determine these special points, an experimental protocol has been built and pertinent parameters have been integrated to the model. According to these studies, we propose an original method that takes into account the previous learning modules to determine the entire trajectory of the wrist in order to reach the same object placed behind two successive obstacles. The aim of this approach is to understand better the impact of experience in a task realisation and show that learning can be performed from previous initiation. Some results (applied to obstacle avoidance task) show the efficiency of the proposed method.

Keywords:

• Reach motion
• obstacle avoidance neural network
• via point