---
publication_id: "OT-2026-000001178"
title: "Machines learning assembly tasks using pre-trained skill libraries"
published_at: "2026-09-15T19:03:58.705306Z"
canonical_url: "https://opentechnical.org/p/OT-2026-000001178"
categories:
  - "robotics-automation/robot-architectures"
keywords:
  - "b25j9/1661"
  - "b25j9/1661"
  - "b25j9/16"
  - "b25j9/163"
  - "b25j9/163"
---

# Machines learning assembly tasks using pre-trained skill libraries

## Abstract

To determine the actions of a machine assembly system (e.g., robotic assembly system), a relevant skill policy can be selected from a library of skill policies. The target task can be used to determine target task information, specifically, the geometry of one or more objects (e.g., parts) of the assembly task, one or more dynamic parameters of the objects, and expert actions that are specified. These parameters can be used as input into the skill library to predict the most relevant skill policy. The target task and the source task (e.g., a potential skill policy) can be compared on the metrics of geometry, dynamics, and expert actions. The best match skill policy can be returned as the relevant skill policy. This policy can then be modified, such as fine-tuning, to improve the real-world assembly outcomes.