Title:	Robot Skill Learning
DNr:	NAISS 2024/5-150
Project Type:	NAISS Medium Compute
Principal Investigator:	Elin Anna Topp <elin_anna.topp@cs.lth.se>
Affiliation:	Lunds universitet
Duration:	2024-04-01 – 2025-04-01
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

Our research field is Reinforcement Learning (RL) for robotics with different modes (e.g. language, sound, ...) and mechanisms (e.g. attention, curiosity, ...) and {task, skill, visual, knowledge} representations (e.g. embeddings, triples, ontology, graphs, ...). We make use of Neural Architecture Search (NAS) of medium to large Neural Networks (e.g. CNN, Transformer, ...). To benchmark, we evaluate our approach against implementations of methods from other authors to determine and compare the performance. We plan to apply explainability algorithms (e.g. Grad-CAM, RISE, SHAP, ... ) to check for plausibility of the agent's actions and reasoning.