In our recent work (Cully et al., Nature, 2015 / see the video below), we demonstrated how a 6-legged robot can recover from an unforeseen damage conditions in less than 2 minutes. This novel machine learning algorithm opens many new possibilities to make robots more reliable and, overall, more adaptive.
But, what would happen if the robot is damaged again? Should the robot forget everything it has learned when trying to cope with the first damage condition? If not, what should it keep? And, what if the adaptation was not an actual damage but a perturbation from the enviroment, for instance a different terrain? How could the robot recognize that "the situations looks like something that it is has seen before" if it goes back to the initial terrain?
The sucessful applicant will design new experiments and new algorithms to answer these questions. He/she will have access to the facilities of the lab (two 6-legged robots, Optitrack motion capture system, etc.) and he/she will be integrated in a highly-motivated team dedicated to leveraging trial-and-error learning to make robots that can adapt to anything (see: http://www.resibots.eu).
The ideal applicant loves robots. He/she has an appetite for machine learning algorithms and (modern) C++.
<strong>Video:</strong> https://www.youtube.com/watch?v=T-c17RKh3uE
<strong>References:</strong>
[1] Cully, Antoine, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. “Robots That Can Adapt like Animals.” Nature 521, no. 7553 (May 27, 2015): 503–7. doi:10.1038/nature14422.
<strong>POUR POSTULER</strong>
Envoyer e-mail + lettre de motivation à jean-baptiste.mouret@inria.fr
<strong>Informations supplémentaires</strong>
http://pages.isir.upmc.fr/~mouret/