Data-driven extraction of neuro-symbolic operators

The project aims to learn neuro-symbolic operators that are effective in planning sequence of actions in direct and inverse tasks. The data obtained from human action observations as well as robot’s own action executions, will be used to learn associations between low-level sensorimotor observations and high-level symbolic representations. The extracted symbols should be useful in planning, verification and inversion, therefore the extraction algorithms will be biased considering their
effectiveness for such tasks.