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

TL;DR

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.

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.

Figures (12)

• Figure 1: Robotic control-method hierarchy: Each level increases flexibility, intelligence and generalisation capabilities, while reducing manual engineering effort and required expertise for operation and adaptation.
• Figure 2: Publication increase. Visualising the amount of publications gathered per category.
• Figure 3: Table of Contents. Highlighting preliminary sections in red, background in purple and the main review in green.
• Figure 4: Overview of all utilised corpora and number of considered publications, as presented within this section.
• Figure 5: General Literature Acquisition Pipeline Procedure: Manual user involvement is required only for the Manual Refinement steps (highlighted in pink); all other processes execute automatically based on user-input. Only the Query Proposal and Filter Process modules leverage LLMs, while the DB-Search is fully deterministic.