Bigwave Robotics launches Hyper Foundry for robot AI development

Bigwave Robotics launched Hyper Foundry on Oct. 20 to unify robot data collection, model training, validation and deployment in one platform for Physical AI development. The company unveiled the system in Seoul and says it is aimed at researchers and industrial teams testing humanoid and collaborative robots.

Why it matters: - Bigwave Robotics is trying to remove a major bottleneck in Physical AI: moving from a robot demo to a deployable industrial system. - The platform could help manufacturers, logistics operators and research teams manage data, training and deployment in one workflow. - Bigwave is positioning itself deeper in the software layer of robotics, not just systems integration.

What happened: - Bigwave Robotics launched Hyper Foundry on Oct. 20, 2026. - Hyper Foundry is a Physical AI development platform that connects robot data collection, model training, validation and deployment in a single environment. - The platform is designed for teams building AI systems that interact with the physical world through robots. - Bigwave presented Hyper Foundry at BIGWAVE SHIFT 2026 - Industrial Physical AI Summit in Seoul on Oct. 22. - The event included a demonstration linking the platform to a humanoid robot and showing a workflow from robot data collection to AI training and robot motion.

The details: - Hyper Foundry supports imitation learning and reinforcement learning for humanoid and collaborative robots. - The platform links stages that are often handled by separate systems, including behavioral data capture, model evaluation and robot deployment. - Bigwave says the platform follows an iterative loop: collect robot data, train and validate a model, deploy it to a robot, then use the resulting records for further development. - Hyper Foundry includes four modules. - Data Studio collects and synchronizes behavioral data from different robots. - Model Studio supports model development using imitation learning and reinforcement learning. - Mission Studio connects trained models with physical robots for deployment and execution. - Data Library manages datasets, models and deployment histories across the development process. - Research institutions can use the platform to collect robot data and develop and evaluate models. - Industrial organizations can use task-specific data to test whether trained models are suitable for manufacturing and logistics environments. - Hyper Foundry supports on-premises data management. - The platform can be configured for an organization’s robots and research infrastructure. - Bigwave said the setup is intended for organizations that need to keep development resources and robot data inside their own facilities. - The platform is available with additional product information and an introductory video at More information.

Between the lines: - The launch extends Bigwave’s work from robot adoption and automation integration into software infrastructure for developing and deploying robot intelligence. - The company is building around a persistent industry problem: robot AI systems often need real hardware testing before they can be used in industrial settings. - Bigwave’s messaging suggests the company sees Physical AI as a workflow problem, not only a model-training problem. - Bigwave and Rainbow Robotics also signed a memorandum of understanding to develop integrated hardware-software solutions and pursue opportunities in the North American robotics market.

What's next: - Bigwave will likely use Hyper Foundry to support further Physical AI deployments and industrial testing. - The company’s collaboration with Rainbow Robotics points to future hardware-software integrations and possible expansion in North America. - Bigwave is likely to keep pushing its platform approach alongside its existing robotics offerings, including SOLlink for multi-robot operations.

The bottom line: - Hyper Foundry gives Bigwave Robotics a single platform for robot data, AI training and deployment, and it marks a broader push into the infrastructure layer of Physical AI.