Industrial robots are being asked to perform increasingly complex tasks in advanced manufacturing and industrial environments to meet growing demands driven by labor shortages and operational complexity. However, traditional programming methods remain slow and inflexible, and often require expert knowledge - particularly in complex domains such as robotics or computer vision. Today, this is starting to change. What once required meticulous programming to adapt industrial robots for specific manufacturing processes and tasks can now be replaced with Robotic Foundation Models (RFMs).

RFMs, similar to large language models for text, are generative AI models trained from a dataset that enables robots to perform and predict a variety of tasks with high accuracy and adapt to changing conditions without reprogramming. As a result, scaling industrial robots across industrial environments is possible to a new degree through combining advanced AI, data-driven learning, and purpose-built cloud platforms.

Building Intelligence from Extensive Datasets

Large datasets allow robots to sense, understand, and interact with the physical world. Extensive datasets, built on real robotic data from industrial applications and the data farm, simulations, and human data, enable them to recognize patterns, understand complex relationships, and perform highly autonomous and accurate predictions and actions. 

By measuring and detecting environmental inputs through physical sensors and converting them into data points, intelligent systems can execute movements with precision and complete tasks more efficiently. Human demonstrations also provide high-quality data by supporting imitation learning, which enables systems to learn specific behaviors directly.

Converting Data to Physical Capabilities

From the collection of data, Robotic Foundation Models are trained to have a comprehensive understanding of robotic functions, rather than individual tasks. This allows robots to operate autonomously without the need for complex traditional programming.

For example, after receiving verbal instructions to replace a hard drive in a server rack, an AI-driven robot can autonomously navigate the rack, locate the drive bays, remove the existing drives, and insert the new ones into the empty slots with precision. This highly variable task is possible due to foundation models that permit robots to perform specific tasks they haven’t been programmed or trained to do. The training goes beyond individual actions.

By leveraging imitation learning and reinforcement learning, the system can learn behaviors directly from demonstrations, bypassing the complexities of classical robotics. The data used to train these models forms the “brain” of the system, allowing it to convert digital input into precise, autonomous physical actions.

The Need for an Efficient Cloud Infrastructure

While foundation models allow for greater flexibility and shorter downtime within production, vast amounts of data must be stored and processed. Bigger datasets require more robust computing systems, expanded storage, and faster network connections since every datapoint from sensors, simulations, and human input must be analyzed in real time for robots to perform reliably. In the absence of adequate infrastructure, models exhibit slower training times, delayed analyses, and reduced ability to adapt to new tasks. Moreover, inference also requires appropriate edge computing hardware. Therefore, to support these autonomous operations, an efficient and scalable computing architecture is critical.

An efficient cloud infrastructure combines large-scale GPU clusters, high-performance storage, and high-speed networking, giving RFMs access to data in real time. In addition to processing data, a powerful cloud platform can also generate artificial data to train models by creating simulations, like different object positions and layouts, or rare safety incidents. This, in turn, allows the system to learn faster and adapt to more circumstances.  

Bringing Industrial AI to Life

As AI increasingly enters workflows and supply chains across industries, industrial AI is innovating rapidly, paving the way for a new era of flexible, efficient, and resilient automation. In robotics and manufacturing sectors, this progress allows robots to meet growing demands and adapt with greater flexibility, while striving for seamless integration alongside humans, enabling industrial sectors to scale AI and robots to new heights.

This capability depends on three interconnected elements: large and diverse datasets, training methods, such as imitation learning and reinforcement learning, and scalable cloud infrastructure capable of processing data in real time. Together, they allow robots to learn from data, adapt to their environment, and perform a wider range of tasks reliably. Understanding how these components work together is essential for deploying intelligent robotic systems safely and efficiently at scale.

Shomit Manapure is a seasoned operations leader with 25+ years of experience in technology scaling, managing multi-billion-dollar budget and driving large-scale greenfield projects across Asia, Europe and North America.

Prior to joining Agile Robots, Shomit spent 14 years at Apple in leadership roles as part of the Worldwide Operations team, where he played a key role in scaling Apple’s global supply chain and manufacturing footprint globally.