Title:	Creating grounded and disentangled representation of robot skills
DNr:	SNIC 2021/22-910
Project Type:	SNIC Small Compute
Principal Investigator:	Alexander Dürr <alexander.durr@cs.lth.se>
Affiliation:	Lunds universitet
Duration:	2021-11-17 – 2022-12-01
Classification:	10207
Homepage:	https://portal.research.lu.se/portal/en/projects/reinforcement-learning-in-continuous-spaces-with-interactively-acquired-knowledgebased-models(6bcfa32c-e9c7-468a-9acb-f433ad98a06a).html
Keywords:

Abstract

We want to get familiar with the Tetralith resources as we attend(ed) the Singularity workshop on November 17. We want to explore the possibility to use Tetralith's setup: Singularity, GPUs, ThinLink. Our setup involves ROS and parallel simulations (-> Singularity) for which we sometimes would like to have GUI access (->ThinLink). We want to test a small scale Neural Architecture search (->GPUs). Long term, we want to develop our setup on Tetralith to ease a possible later move to Berzelius if the model scales better with more GPU power.