FMC³, Vector, PEM Motion form joint venture for embodied AI data infrastructure

Vector Informatik, PEM Motion, and FMC³ Robotics have signed an MoU to establish a joint venture focused on building standardized data infrastructure for industrial embodied AI. The collaboration addresses a critical gap: robots moving from pilot projects to production environments require high-quality physical-world data and seamless infrastructure for training, validation, and continuous improvement.

At the Humanoid Robots Summit 2026 in Stuttgart, Vector Informatik, PEM Motion, and FMC³ Robotics signed a memorandum of understanding to jointly develop standardized data infrastructure for embodied AI in manufacturing. The partners plan to establish a joint venture in Germany.

The collaboration targets a fundamental bottleneck in industrial AI deployment. As robots transition from controlled pilots to real production floors, performance depends not just on models and hardware, but on unified data pipelines that connect collection, processing, training, validation, deployment, and operational feedback. The partners aim to build that infrastructure around standardized protocols supporting multimodal data.

Each partner brings distinct capabilities. PEM Motion contributes production engineering and manufacturing implementation expertise. Vector supplies embedded systems knowledge and standardized data protocols — critical for compliance and governance in industrial settings. FMC³ Robotics provides its embodied AI brain platform and data collection tools designed for physical-world interaction.

The partnership reflects a broader recognition that embodied AI scaling is as much a data and systems engineering challenge as it is a model training problem. Robots operating in unstructured environments generate massive volumes of sensor data that must be validated, labeled, and fed back into training loops while maintaining safety and security standards.

The joint venture will focus initially on manufacturing use cases, particularly in small and medium-sized enterprises where standardized, plug-and-play AI infrastructure could lower adoption barriers. The partners did not disclose funding details or a timeline for the venture's formal establishment.