The International Federation of Robotics has published a comprehensive position paper examining how artificial intelligence is fundamentally reshaping robotics, transforming AI from a supporting technology into a core enabler expanding automation beyond fixed tasks into logistics, manufacturing, and service environments. The report analyzes commercial adoption, technological limits, and regulatory pressures across industrial and service robotics sectors.
While AI has quietly underpinned robotics for decades, enabling machines to handle production variability and operate safely in shared environments, recent advances in data availability, computing power, and model design have pushed AI into a central rather than supporting role. This shift expands the range of tasks robots can perform while lowering barriers to adoption across industries.
Deep-learning computer vision now allows robots to see and interpret visual data for object recognition, barcode reading, sorting, and real-time production-line monitoring. Supervised learning powers defect detection, predictive maintenance, quality inspection, and process optimization. Natural language processing enables collaborative and service robots to understand and respond to spoken or written commands.
Three sectors currently dominate AI robotics integration. Logistics and warehousing lead adoption driven by high investment readiness, controlled environments, and growing demand. Applications span from warehouse navigation to complete supply chain management, with mobile robots using sensor fusion combining LiDAR and cameras for simultaneous localization and mapping.
Manufacturing and industrial automation represent focal points for investment as companies streamline operations and enhance output quality. The sector spans automotive, electronics, pharmaceuticals, and general industries, encompassing high-skill production processes, factory automation systems, and precision assembly tasks.
Service sectors increasingly adopt AI robotics to support human-robot interaction, allowing natural communication and improving usability and personalization. Rising labor costs and worker shortages, particularly in post-pandemic markets where recruitment lags demand, drive adoption.
Emerging Technologies : Reinforcement learning, though still emerging in industrial settings, gains traction for robot motion and path planning, grasping, and adaptive control, where robots learn through trial and error in dynamic environments. Generative AI represents the next step, potentially creating code for entire robotic functions based on natural language instructions, though developers must rigorously test AI-generated code quality.
Physical AI marks a significant trend, with robot and chip manufacturers investing in dedicated hardware and software simulating real-world environments. This approach allows robots to train in virtual environments and operate through experience rather than procedural programming, drawing attention from major technology players and governments worldwide.
The position paper provides industry stakeholders with framework for understanding AI’s role in robotics development, highlighting both transformative potential and critical challenges requiring attention as the technology scales from specialized applications to widespread adoption.
