IFR position paper on AI in robotics released

AI in Robotics - Trends, Challenges, Commercial Applications

AI is transforming the field of robotics at a rapid pace. Integrating AI into robotics enhances capabilities, increases efficiency and improves adaptability.

How AI is supporting robotics

Powered by deep learning, computer vision enables robots to “see” and interpret visual data for tasks like object recognition, barcode reading, sorting, and real-time monitoring on production lines. Especially supervised learning is commonly used for tasks like defect detection, predictive maintenance, quality inspection, and process optimization. Robots learn from labeled data to make accurate predictions or classifications.
Used in collaborative or service robots that interact with humans, Natural language processing (NLP) allows robots to understand and respond to spoken or written commands.
In mobile robotics, AI is used to combine data from multiple sensors (e.g., LiDAR, cameras) to enable SLAM (Simultaneous Localization and Mapping) navigation in warehouses or manufacturing floors.
Though still emerging in industrial settings, Reinforcement Learning (RL) is being increasingly used for robot motion and path planning, grasping, and adaptive control, where robots learn by trial and error to improve performance in dynamic environments. The next step is for generative AI. It can, for example, change the way that coding is done. It will do this by creating code for entire functions that a robot will perform based on natural language instructions.

Industries at the forefront

There are currently several key sectors leading the way in integrating AI and robotics:

• Logistics and warehousing are frequently cited as the leading domain. This is driven by high demand, available investment, and relatively controlled environments. Fields of adoption include logistics, warehousing and intralogistics, and the broader supply chain. The sector attracts attention for its resilience and growth potential.
• The manufacturing and industrial automation sector is a focal point for investment: As companies seek to streamline operations and enhance output quality, AI and robotics are playing an increasingly central role in modern manufacturing strategies. The sector is spanning a broad array of industries. This includes automotive, electronics, and the general industries like pharma. The category encompasses high-skill production processes, factory automation systems, and precision assembly tasks.
• The service sector is among the leading customers adopting AI and robotics. AI is supporting human-robot interaction, allowing for example a natural communication and increasing the usability and personalization of the robots. This trend is driven by rising costs and a shortage of workers, particularly in post-pandemic markets, where recruitment has lagged demand. Restaurants, for example, are experimenting with robotic servers and kitchen assistants. The future lies in hybrid models where robots handle repetitive tasks and humans deliver the personal touch.

AI is reshaping work

Robot installations are traditionally taking over physically demanding and repetitive tasks, freeing employees from harsh working conditions. As AI tools become more common, new roles emerge for supervising, analyzing and making decisions. This is creating new jobs, including AI engineers, data scientists, machine learning specialists, and ethicists. To fill these new jobs, there is a growing demand for digital and cognitive skills, such as coding and data literacy, as well as critical thinking.

In future, AI in robotics will further influence how teams work, how decisions are made, and how performance is monitored. This can improve workflows but may also raise concerns about employee surveillance or reduced autonomy. Companies and governments are pushing reskilling and upskilling programs to help workers remain competitive in an AI-driven economy. AI enhances efficiency, reduces errors, and increases output across many industries. This can lead to economic growth but also puts pressure on businesses and workers to continuously adapt and innovate[1].

Macroeconomic Trends impacting AI

The future of AI and robotics is being shaped by a series of macro trends.

• Economic and Social Pressure: Geopolitical instability caused by global trade tensions and rising tariffs is pushing up manufacturing costs and encouraging companies to increase efficiency by using AI powered robots. This trend is further fueled by labor shortages. Robots equipped with AI can augment human teams and stabilize productivity.
• Investment and Strategic Importance: AI and robotics are becoming central to corporate strategy, with funding flowing into research, education and computing infrastructure. Executives see the technologies as critical to long-term competitiveness.
• Safety and Governance: Growing scrutiny of AI systems is prompting calls for greater transparency and safeguards against bias. Regulators are tightening rules on data privacy, adding pressure on companies to comply.
• Cybersecurity: The growing integration of robotics with AI is creating new cybersecurity vulnerabilities, as AI-enabled robots typically are connected to the cloud and thus become attackable. Companies and governments need to set clear safety standards and build strong public–private partnerships to anticipate and mitigate new cybersecurity risks in robotics.

Safety concerns

There are certain safety concerns applying to AI used in the context of physical robots that require attention by developers and users alike. This includes data poisoning and training with compromised datasets, addressing biases, and the unpredictability of autonomous systems. Quality of AI generated code needs to be ensured.

Malfunctions of the AI in the physical world can have more severe consequences and the physical safety during human-robot collaboration must be guaranteed at all times.

Addressing Sustainability

A focus on sustainability will positively shape AI development in robotics, driving efficiency, extending robot lifespan, aligning with ethical goals, and enabling green transformation across industries. However, it will also push the field to confront the energy cost of AI itself. There are concerns over the ecological cost of training large models. Deep learning's carbon footprint, for example, may conflict with sustainability unless mitigated.

• Energy Efficiency & Power Optimization addresses concerns around the energy demands of large AI models. This includes reducing energy consumption of robots and AI systems such as trajectory optimization and efficient processing.
• Waste Reduction & Circular Economy supports goals such as automated sorting, reuse, and recycling. AI is used to minimize material waste, optimize resource usage, and reduce scrap in manufacturing.
• Longevity, Maintenance & Reliability leads to longer robot lifespans, easier servicing, and lower resource demand over time. AI enables predictive maintenance and smarter robot operation.