Probabilistic Movement Primitive (ProMP) is a recent concept to learn trajectories in robotics [1]. Starting with a few demonstrations from a teacher, a ProMP builds a probabilistic distribution of the demonstrations. This representation allows other algorithms to infer the best trajectory depending on the context (e.g. a movement by an operator) [2].
Instead of starting with demonstrations, we would like to generate the distribution of potential trajectories with an algorithm; we think the MAP-Elites algorithm [3][4], a novel evolutionary algorithm that we published last year, could be the ideal algorithm to do so.
The objective of the internship is to mix Probabilistic Motion Primitives with MAP-Elites. Most experiments will be performed on our Kinova robotic arm or with the iCub humanoid robot.
The successful 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++.
<b>References:</b>
[1] Paraschos, Alexandros, Christian Daniel, Jan Peters, and Gerhard Neumann. "Probabilistic movement primitives." In Advances in Neural Information Processing Systems, pp. 2616-2624. 2013.
[2] Maeda, Guilherme, Marco Ewerton, Rudolf Lioutikov, Heni Ben Amor, Jan Peters, and Gerhard Neumann. "Learning interaction for collaborative tasks with probabilistic movement primitives." In Humanoid Robots (Humanoids), 2014 14th IEEE-RAS International Conference on, pp. 527-534. IEEE, 2014.
[3] Mouret, Jean-Baptiste, and Jeff Clune. "Illuminating search spaces by mapping elites." arXiv preprint arXiv:1504.04909 (2015).
[4] 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.