Sopra Steria and FIDAMC Partner on Physical AI Systems
The companies aim to automate complex composite manufacturing processes currently requiring manual labor.
Updated on Oct. 28, 2026 in Robotics
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Sopra Steria and FIDAMC have announced a partnership to develop Physical AI systems for aerospace manufacturing. This research-stage initiative aims to automate complex composite materials production processes that currently rely on manual labor.
Why it matters
Aerospace manufacturing is currently hindered by high manual complexity that traditional robotics struggle to address. This collaboration seeks to bridge the gap between digital simulation and physical execution to accelerate factory automation.
The project utilizes digital twins—virtual replicas of physical systems—to train industrial robots before they enter a live production environment. The performance of these models remains in the research phase rather than established benchmark output.
The players
Sopra Steria
A Paris-based consultancy and digital services firm focused on enterprise software, simulation, and artificial intelligence.
FIDAMC
A Getafe-based research and development center specializing in composite materials science and industrial robotics.
The details
Physical AI refers to systems capable of perceiving, reasoning, and acting within the physical world, moving beyond standard scripted automation. By integrating FIDAMC's robotics expertise with Sopra Steria's digital simulation platforms, the project attempts to teach robots how to handle intricate composite materials. Robots are first trained in a simulated environment to learn manipulation strategies before deployment at the Getafe cell.
Timeline
October 28, 2026: The partnership between Sopra Steria and FIDAMC is formally announced.
The Tech Race
The project attempts to modernize aerospace manufacturing processes that have historically resisted automation. It signals a shift toward deploying AI-driven robotics across the European industrial ecosystem.
Manufacturing engineers and aerospace logistics teams are the primary audiences as this technology evolves from research into a production tool. Practical application remains internal to the R&D cycle at the Getafe site for now.
The takeaway
The success of this collaboration will be measured by whether the simulated training models can reliably handle composite materials in the real-world cell in Getafe. Watch for future technical white papers or shared results from the partners as they attempt to scale these methods for broader industry use.
Further reading
For broader trends in automated assembly and industrial autonomy, visit Robotics.
Source note: This article includes information reported by Compositesworld.
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