Robot Imagination System (RIS)

ris

Description

Take a look at most machine learning algorithms, when trying to get something novel, you’ll most probably get an average off what you already have. Robot Imagination, as described in its first international conference paper, aims to generate novel mental models of objects given a previous phase of semantic supervised training. Some improvements can be seen in posterior contributions. The transition to actions would not be easy, and it eventually lead to the foundations of CGDA research. Additional references include the original J.G.Victores PhD thesis.

Entries:
Towards Robot Imagination Through Object Feature Inference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)., 2013, Tokyo, Japan
Juan G. Victores S. Morante A. Jardon
On Using Humanoid Robot Imagination to Perform the Shortened Token Test
IEEE RAS International Conference on Humanoid Robots (Humanoids 2014), 2014, Madrid, Spain
Juan G. Victores S. Morante A. Jardon
Semantic Action Parameter Inference through Machine Learning Methods
RoboCity2030 12th Workshop: Robótica Cognitiva, 2013, Madrid, Spain
Juan G. Victores S. Morante A. Jardon

Entries:
Robocity16 Open Conference on Future Trends in Robotics
chapter: New trends and challenges in the automatic generation of new tasks for humanoid robots CSIC , ISBN: 978-84-608-8452-1, 2016
R. Fernandez-Fernandez Juan G. Victores C. Balaguer
Robocity16 Open Conference on Future Trends in Robotics
chapter: New trends and challenges in the automatic generation of new tasks for humanoid robots CSIC , ISBN: ISBN: 978-84-608-8452-1, 2016
R. Fernandez-Fernandez Juan G. Victores C. Balaguer

Entries:
Robot Imagination System
Universidad Carlos III de Madrid , 2014
Juan G. Victores

Robot types & applications

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