The project aims at reducing the sample and model complexity of robot learning algorithms. For that, known physical equations are merged with machine learning models in order to adapt faster to unknown environments while quantifying uncertainty. Causal learning will be applied to this world model to generate exploratory behavior and to accelerate the learning processes. Once a world model has been learned, robot behaviors will be optimized using optimal control or reinforcement learning.
Project | PhysWM
Duration:
PhysWM - Learning from Causal Physical World Models
Research Topics
Application fields
- Other
Keyfacts
Publications about the project
Sponsors
BMWK - Federal Ministry for Economic Affairs and Climate Action
