Learning Robots Lab
Robots and AI
 PhD position available!  Apply here
   Main researcher: Jan Lemeire (jan.lemeire@vub.be)

Vision Text    paper "Contextual Qualitative Deterministic Models for Self-Learning Embodied Agents" presented at the  International Workshop on Active Inference (IWAI), 13-15 September 2023 in Ghent, Belgium.


The goal is to let robots learn themselves starting from an ‘empty’ brain to control their world. This will make robots more robust and versatile since they will be able to adapt to new environments and changes in their environment.
Humans are the example. Babies are born without any skills, they even don't know how to control their muscles/limbs. They just have the skill to learn... And become experts in understanding and controlling the world. Better than animals preprogrammed with instincts. Or at least more versatile.

Our strategy is to start with incrementally learning the system's structure—dependencies, causal connections and phase transitions—by doing a qualitative analysis rather than learning a monolithic quantitative model.
The result is a decomposition based on reusable, monotonous components that are then transformed into quantitative models. Learning takes place gradually and is closely intertwined with exploration and exploitation.

Thesis topics
Check our proposals 2023-2024

Current work

We are further working on our result of the IWAI paper.

Then we continue to work on our solution for the REAL Open Ended Learning competition.

Some of our self-made robots


last updated: June, 5th 2023 by Jan Lemeire
Engineering Faculty,