Generative AI in Industrial robotics
In today's business landscape, the adoption of AI has become a given, but productive applications of generative artificial intelligence (AI) are less exciting than the hype suggests. They don’t require a disruptive mindset, but realistic expectations, thoughtful planning, and risk management. Industrial robotics is a good example.
Consider an application which was developed by Hewlett Packard Enterprise and German AI company Aleph Alpha: It’s a generative-AI assistant that increases efficiency and safety when installing, operating and maintaining an industry robot. It acts like a highly specialized service technician who supports the factory staff in solving complex tasks.
When communicating with the AI assistant, the factory staff do not have to adhere to any predefined system or use any specific terminology. The dialogue is also possible in several languages, regardless of the language with which it was trained. A simple example of dialogue would be: “Emergency! How can I stop the robot immediately?” Answer: “Press the emergency stop button. It's the big red button on the top right of the handheld unit.”
The exchange with the AI assistant can also take place via images. For example, when calibrating the robot, an operator takes a picture of a specific calibration mark with a smartphone or tablet and asks if that is the correct calibration position.
These capabilities can contribute significantly to the efficiency and safety of robot operations. The factory staff is not dependent on the help of a specialized service technician for many detailed questions about installation, maintenance or troubleshooting, saving time and money. Furthermore, the AI assistant supports factory personnel in complying with safety regulations and in the event of acute problems, providing crucial information to prevent damage or production downtime.
Running the large language model on a private on-premises infrastructure additionally can help protect trade secrets and avoid cloud dependencies.
The capabilities of such an AI assistant can be extended to the entire production environment of a factory and to the supply chain – for example, by being trained with further technical documentation as well as with information on suppliers, supply agreements, legal terms and regulations, costs, or CO2 emitters.
Making generative AI productive in the enterprise is not fancy. It’s hard, sometimes boring work. It’s not about saving the planet or disrupting an entire industry – it’s about creating incremental savings in day-to-day operations, accelerating business processes, and preventing outages.
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