AI in robotics gains strategic importance
AI in robotics: IFR outlines the next stage of development
AI in robotics is on the verge of transitioning from research to widespread practical use. A new IFR position paper highlights market potentials, key industries, and technological milestones.
Ai in robotics as a growth driver
Ai in robotics is evolving from a supportive technology to a central enabler of modern automation. The international federation of robotics (ifr) has published a new position paper analyzing trends, challenges, and commercial application fields, as the association now reports. For ai-supported robots of the next generation, which are increasingly reaching real-world deployment environments, technology companies and analysts see a long-term market potential of several trillion dollars.
“Ai is rapidly changing robotics,” says takayuki ito, president of the international federation of robotics. “Integrating ai into robotics can improve capabilities, increase efficiencies, and enhance adaptability. This development transforms ai from a supportive technology into a powerful enabler, opening doors for broader use of robotics across all industries.”
Logistics and intralogistics as early adopters
The pioneering industries for ai in robotics include logistics, warehousing, and intralogistics. The sector benefits from high demand, available investment funds, and relatively controllable environments. Ai-supported robotic systems are used throughout the entire supply chain, making the area particularly attractive due to its resilience and growth potential.
Industry automation and manufacturing in focus
According to the IFR, manufacturing and industrial automation are also among the key investment areas. Companies are increasingly relying on AI in robotics to streamline processes and enhance production quality. The range of applications extends from the automotive and electronics industries to general industries such as the pharmaceutical industry. The focus is on highly skilled production processes, factory automation systems, and precise assembly tasks.
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Service sector drives human-robot interaction
The service sector is also establishing itself as an important user industry for AI in robotics. Here, AI particularly supports human-robot interaction, such as through natural communication, personalized processes, and increased user-friendliness. The demand is driven by rising costs and the ongoing labor shortage, especially in areas where job vacancies remain unfilled after the pandemic. For example, restaurant businesses are experimenting with robot waiters and kitchen assistant robots. In the future, hybrid models are expected to prevail, where robots take over repetitive tasks and humans remain responsible for personal interaction.
Physical AI and embodied AI as the next stage of development
A new vision for AI in robotics is emerging through investments by robot and chip manufacturers in specialized hardware and software solutions to simulate real environments. This so-called 'physical AI' allows robots to train themselves in virtual worlds and learn from experiences, rather than being programmed for individual tasks. The associated 'embodied AI' has attracted the interest of major technology companies and government actors worldwide.
International investments and strategic initiatives
In the USA, companies like Amazon, Tesla, and NVIDIA announced record investments, while venture capital is flowing into a growing start-up ecosystem for specialized robotic applications. In Europe, ABB signed an agreement to sell its robotics division to the Japanese SoftBank Group to combine ABB Robotics' AI capabilities with those of SoftBank, as the company reports. In China, the Ministry of Industry and Information Technology has introduced an action plan to accelerate 'embodied AI' and classified it as a strategic future industry.
Outlook on the widespread use of AI in robotics
For the next five to ten years, the IFR expects a widespread use of AI in robotics across numerous application fields. Efficiency improvements, lower error rates, and reduced maintenance costs often promise companies a faster return on investment compared to traditional systems.
With material from the IFR