Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations

2026-03-13

AI Summary

This work proposes a unified framework based on a Skill Graph to address the limitations of traditional robotic assembly systems, which rely heavily on manual engineering and lack capabilities for autonomous integration and continuous optimization. In this framework, robot capabilities are modeled as verb-like skills, each linked to semantic descriptions, executable policies, preconditions, and performance evaluators, thereby enabling seamless integration between high-level semantic planning and low-level execution. For the first time, system configuration, execution, evaluation, and learning are unified within a single Skill Graph representation, facilitating closed-loop, data-driven optimization after deployment. Experimental results demonstrate that the proposed approach significantly improves system integration efficiency and enables autonomous optimization of skill composition, offering a promising pathway toward scalable, reusable, and adaptive robotic assembly systems.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• robotic assembly
• autonomous integration
• continuous improvement
• Skill Graph
• system adaptability

Innovation

• Skill Graph
• robotic assembly
• autonomous integration
• semantic-level planning
• closed-loop improvement