Towards robotic cognition using deep neural network applied in a goalkeeper robot

Developing a vision system combined with a decision system for a humanoid robot, capable of playing soccer in the RoboCup domain, has been proved to be a challenging task. The computational limitations imposed by a embedded computer inside the robot and special conditions, such as the use of colored objects, led teams to use techniques based on color segmentation for vision and conditional statements for decision. However, the current league trend is to insert the robots into more and more realistic environments. This will require the robot to, given an image provided by its camera, to abstract all the information it needs to make a decision regardless of the environment. Most robotic vision systems at RoboCup relies on traditional computer vision techniques: thresholding; windowing; segmentation; and classification that requires hours of labeling to training and testing. This paper proposes a system that does not require to locate objects coordinates in the image - a deep neural network will identify most important features resulting as an output that is a decision. Results show that Deep Neural Network (DNNs) enabled the system to be more simple, robust (with less parameters to be set by hand) and achieved a performance that is compatible with the dynamics of the humanoid robot soccer. This system was tested in a real robot and simulator.

[1]  Hajime Asama,et al.  Development of open humanoid platform DARwIn-OP , 2011, SICE Annual Conference 2011.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[4]  Reinaldo A. C. Bianchi,et al.  Hardware and Software Aspects of the Design and Assembly of a New Humanoid Robot for RoboCup Soccer , 2014, 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol.

[5]  Stefan Wermter,et al.  Ball Localization for Robocup Soccer Using Convolutional Neural Networks , 2016, RoboCup.

[6]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Yann LeCun,et al.  Generalization and network design strategies , 1989 .

[8]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[9]  Jürgen Schmidhuber,et al.  A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots , 2016, IEEE Robotics and Automation Letters.

[10]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[11]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[12]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[13]  Thomas S. Huang,et al.  Image processing , 1971 .

[14]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[15]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[16]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.