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.

This project extracts parameterised skills from human-level specifications (e.g., CAD and MTM) and human demonstrations. For that, the following methodological steps will be taken. First, a palette of basic robotic skills will be constructed. Then, human demonstrations are recognized as a sequence of segmented skills. Thus, skills and their task relevant parameters are extracted; the parameterization enables fast generalisation of the skill to different use cases.

Applying LLM or Visual Language Foundational Model in a selected robotics task

Objective:

The goal of this project is to evaluate the political compass of large la