Robotic Foundation Models for Industrial Control: A Comprehensive Survey and Readiness Assessment Framework

TL;DR

This paper provides a comprehensive survey and assessment framework for robotic foundation models (RFMs), analyzing their industrial readiness, limitations, and the specific requirements for deployment in real-world industrial settings.

Contribution

It introduces a novel assessment framework with 149 criteria to evaluate RFMs' industrial applicability and applies it to 324 models, revealing limited maturity and uneven coverage.

Findings

Most RFMs meet only a subset of industrial criteria.

High-rated models show narrow strengths rather than comprehensive capabilities.

Progress requires integrating safety, real-time operation, perception, and cost considerations.

Abstract

Robotic foundation models (RFMs) are emerging as a promising route towards flexible, instruction- and demonstration-driven robot control, however, a critical investigation of their industrial applicability is still lacking. This survey gives an extensive overview over the RFM-landscape and analyses, driven by concrete implications, how industrial domains and use cases shape the requirements of RFMs, with particular focus on collaborative robot platforms, heterogeneous sensing and actuation, edge-computing constraints, and safety-critical operation. We synthesise industrial deployment perspectives into eleven interdependent implications and operationalise them into an assessment framework comprising a catalogue of 149 concrete criteria, spanning both model capabilities and ecosystem requirements. Using this framework, we evaluate 324 manipulation-capable RFMs via 48,276 criterion-level decisions obtained via a conservative LLM-assisted evaluation pipeline, validated against expert judgements. The results indicate that industrial maturity is limited and uneven: even the highest-rated models satisfy only a fraction of criteria and typically exhibit narrow implication-specific peaks rather than integrated coverage. We conclude that progress towards industry-grade RFMs depends less on isolated benchmark successes than on systematic incorporation of safety, real-time feasibility, robust perception, interaction, and cost-effective system integration into auditable deployment stacks.