Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
This addresses the challenge of making robotic assembly more adaptive and reusable, though it appears incremental by building on existing skill-based representations.
The paper tackles the problem of robotic assembly systems requiring manual effort for integration and improvement by introducing a Skill Graph framework that organizes robot capabilities for semantic planning and execution, enabling rapid integration and continuous performance refinement.
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.