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An evolved learning mechanism for teaching a robot to foveate
| Venue: | in Proc. of the 9th Int. Symp. on Artificial Life and Robotics (AROB-9 |
| Citations: | 2 - 1 self |
BibTeX
@INPROCEEDINGS{Gómez_anevolved,
author = {Gabriel Gómez and Peter Eggenberger Hotz and Artificial Intelligence Laboratory},
title = {An evolved learning mechanism for teaching a robot to foveate},
booktitle = {in Proc. of the 9th Int. Symp. on Artificial Life and Robotics (AROB-9},
year = {},
pages = {655--658}
}
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Abstract
We showed in a previous work that an artificial evolutionary system whose task was to track a light source was able not only to evolve and grow a neural network, but was also able to evolve learning mechanisms. The evolved neural network was then transferred to a robotic system consistent of a camera mounted on the gripper of a robot arm with results comparable to the ones achieved by the simulation (Eggenberger et al.[1]). In this paper we continued testing the evolved controller in the real-world increasing the sensorymotor capabilities and the robot’s task as follows: (a) The visual system was enhanced to detect color and movement in the environment and a proprioceptive system was added to have feedback of the arm movements. (b) The number of degrees of freedom (DOF) of the robot was increased from two to three. (c) The position of the cameras was fixed and the same underlying principles were used to teach the robot arm in front of the cameras to move a colored object from an initial location at the periphery of the visual field to the center of it. The arm could solve the task not only for two DOF, but also for three DOF. 1
Keyphrases
evolved learning mechanism robot arm artificial evolutionary system visual system visual field robot task colored object arm movement light source proprioceptive system evolved neural network previous work underlying principle neural network initial location sensorymotor capability robotic system consistent evolved controller
