Abstract Hardware Grounding towards the Automated Design of Automation Systems

TL;DR

This paper introduces an automated framework for designing automation systems by aligning human semantic knowledge with hardware, addressing heterogeneity and user preferences to streamline system creation.

Contribution

It proposes the abstract hardware grounding problem and develops a hybrid data-driven and principle-based framework for automated system design.

Findings

Successfully designed self-driving laboratories for scientific discovery

Produced compact, customized automation systems with no redundancy

Demonstrated potential to democratize automation system design

Abstract

Crafting automation systems tailored for specific domains requires aligning the space of human experts' semantics with the space of robot executable actions, and scheduling the required resources and system layout accordingly. Regrettably, there are three major gaps, fine-grained domain-specific knowledge injection, heterogeneity between human knowledge and robot instructions, and diversity of users' preferences, resulting automation system design a case-by-case and labour-intensive effort, thus hindering the democratization of automation. We refer to this challenging alignment as the abstract hardware grounding problem, where we firstly regard the procedural operations in humans' semantics space as the abstraction of hardware requirements, then we ground such abstractions to instantiated hardware devices, subject to constraints and preferences in the real world -- optimizing this problem is essentially standardizing and automating the design of automation systems. On this basis, we develop an automated design framework in a hybrid data-driven and principle-derived fashion. Results on designing self-driving laboratories for enhancing experiment-driven scientific discovery suggest our framework's potential to produce compact systems that fully satisfy domain-specific and user-customized requirements with no redundancy.