Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations

TL;DR

This paper introduces a Skill Graph framework that enables autonomous integration, execution, and continuous improvement of robotic assembly systems by linking semantic skill descriptions with executable policies and evaluation mechanisms.

Contribution

The paper presents a novel Skill Graph representation that unifies system integration, execution, and learning for adaptive robotic assembly, reducing manual engineering efforts.

Findings

Supports rapid system integration through semantic planning

Enables systematic data collection for performance improvement

Unifies configuration, execution, and learning in a single framework

Abstract

Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This paper presents a framework for autonomous integration and continuous improvement of robotic assembly systems based on Skill Graph representations. A Skill Graph organizes robot capabilities as verb-based skills, explicitly linking semantic descriptions (verbs and nouns) with executable policies, pre-conditions, post-conditions, and evaluators. We show how Skill Graphs enable rapid system integration by supporting semantic-level planning over skills, while simultaneously grounding execution through well-defined interfaces to robot controllers and perception modules. After initial deployment, the same Skill Graph structure supports systematic data collection and closed-loop performance improvement, enabling iterative refinement of skills and their composition. We demonstrate how this approach unifies system configuration, execution, evaluation, and learning within a single representation, providing a scalable pathway toward adaptive and reusable robotic assembly systems. The code is at https://github.com/intelligent-control-lab/AIDF.