Insider Brief
- The International Federation of Robotics said artificial intelligence is becoming a core enabler of robotics adoption, expanding automation beyond fixed tasks into logistics, manufacturing, and service environments, while remaining constrained by safety, reliability, and integration challenges.
- The report finds that established AI techniques such as computer vision, supervised learning, and sensor fusion are driving most commercial value today, with generative AI and reinforcement learning still largely limited to supervised and task-specific deployments.
- The federation warned that future growth will hinge on cybersecurity, energy efficiency, workforce reskilling, and fragmented global regulation, arguing that disciplined deployment and trust—not rapid experimentation—will determine AI’s long-term impact on robotics.
Artificial intelligence is no longer an add-on in robotics but a structural shift that is redefining how machines are built, deployed and governed, according to a new position paper from the International Federation of Robotics that examines commercial adoption, technological limits and regulatory pressure across industrial and service robotics.
The report argues that AI has quietly underpinned robotics for decades, enabling machines to cope with variability in production and operate safely in shared environments. What has changed is scale. Advances in data availability, computing power and model design are pushing AI from a supporting role into a central enabler, expanding the range of tasks robots can perform and lowering barriers to adoption across industries.
As artificial intelligence becomes a core building block in robotics, several capabilities are already being used today, the report notes. Machine vision is central to this shift, enabling robots to inspect quality, recognize and classify objects, and perform tasks such as bin picking and sorting with greater precision, while real-time visual feedback allows machines to adapt and improve performance. Autonomous mobility is also advancing rapidly, as AI-powered navigation and SLAM technologies enable robots, drones, and guided vehicles to move reliably through dynamic environments across logistics, manufacturing, and service operations.
AI is also reshaping how robotic systems are maintained and developed. Predictive maintenance uses real-time diagnostics and data analysis to anticipate equipment failures, reduce downtime, and extend asset life. At the same time, simulation, digital twins, and virtual engineering tools are becoming integral to robotics development, allowing systems to be designed, tested, and commissioned in software before physical deployment, cutting costs, reducing risk, and accelerating time-to-market.
As manufacturers are increasingly using artificial intelligence to enhance automation, with the IFR report pointing out that most benefits coming from six core AI subfields that expand their capabilities and flexibility:
- Physical AI: Integrates sensory data from the environment so robots can understand and respond to real-world conditions.
- Machine learning: Used to predict equipment failures and optimize production schedules, improving uptime and efficiency.
- Computer vision: Applies machine-learning models to detect defects and guide robotic arms with greater precision.
- Reinforcement learning: Trains robots through trial and error and is increasingly applied to complex, adaptive tasks.
- Natural language processing: Enables voice-controlled machinery and multilingual support interfaces on factory floors.
- Large language models: Automate documentation, support engineering troubleshooting, and streamline operational decision-making.
Where AI-enabled Robots are Gaining Ground
Adoption is uneven, with certain sectors pulling ahead due to economics and operating conditions.
According to the IFR, industries at the forefront include:
- Logistics and warehousing, where structured environments, labor shortages, and high throughput demands have driven rapid uptake of autonomous mobile robots and AI-enabled navigation.
- Manufacturing and industrial automation, spanning automotive, electronics, pharmaceuticals and general industry, where AI supports precision assembly, inspection, and predictive maintenance.
- Service sectors, including retail, hospitality, and healthcare, where robots are increasingly used to handle repetitive tasks as staffing pressures persist.
In these settings, AI is not replacing automation but extending it, allowing robots to handle higher mix, lower volume operations that were previously uneconomical to automate.
AI is Reshaping Work, Not Eliminating It
The report rejects a simple displacement narrative. Robots continue to take on physically demanding and repetitive tasks, but AI is shifting human roles toward supervision, analysis and decision-making.
New demand is emerging for skills such as data literacy, systems oversight, and AI model management. At the same time, the federation highlights concerns about workplace monitoring, reduced autonomy, and the need for continuous reskilling as AI-driven systems become more embedded in daily operations.
Governments and companies are responding with training and upskilling programs, but the pace of technological change is increasing pressure on both workers and employers to adapt.
Macroeconomic Forces Pushing Adoption
Beyond technology, the report identifies several macro trends accelerating AI adoption in robotics:
- Labor shortages and rising costs, which are pushing companies to seek productivity gains through automation.
- Geopolitical and trade pressures, including tariffs and supply-chain disruption, which increase the incentive to improve efficiency and resilience.
- Strategic investment, as AI and robotics become central to long-term competitiveness, drawing funding into computing infrastructure, education, and R&D.
These forces, the IFR argues, are making AI-enabled robotics less optional and more foundational to industrial strategy.
Safety, Security, and Trust Gaps
As autonomy increases, so do risks. The report devotes significant attention to safety and security challenges that could constrain deployment if left unaddressed.
Key concerns include:
- Cybersecurity, as cloud-connected robots expand the attack surface for hacking, system hijacking, and adversarial manipulation.
- Data privacy, given the volume of video, audio, and sensor data robots collect in workplaces and public settings.
- Model transparency and accountability, as deep learning systems operate as “black boxes,” complicating liability when failures occur.
- Physical safety, particularly in collaborative environments where unpredictable AI behavior can increase the risk of injury.
The federation emphasizes the need to isolate safety-critical systems from AI functions, strengthen validation and testing, and establish clear liability frameworks as robots move closer to humans.
Sustainability and Energy Trade-Offs
AI is also reshaping sustainability debates in robotics. On one hand, AI enables predictive maintenance, longer robot lifespans, waste reduction, and more efficient resource use. On the other, training and operating large models carries an energy cost that can conflict with environmental goals.
The report highlights energy-efficient processing, trajectory optimization, and circular-economy applications as areas where AI can offset its own footprint, but notes that sustainability will increasingly shape design and deployment decisions.
Regulation is Fragmenting Globally
Governance remains uneven. Europe is advancing risk-based oversight through the EU AI Act, adding to existing data protection and cybersecurity rules. China has built a comprehensive framework covering data security, generative AI, and corporate accountability. The United States, by contrast, continues to rely on a patchwork of federal guidance and state-level laws, creating regulatory uncertainty for developers and operators.
The IFR warns that inconsistent standards could slow deployment and increase compliance costs, particularly for global manufacturers.
Outlook: Steady Progress, Not Sudden Transformation
Looking ahead to 2030–2035, the federation expects AI to become a default feature in most robotic systems, improving return on investment through higher efficiency, lower error rates, and reduced downtime. Advances in simulation, digital twins, and virtual commissioning are likely to shorten development cycles and lower deployment risk.
Beyond that horizon, the report outlines a longer-term shift toward more general-purpose and mobile robots, including humanoid platforms. These systems promise flexibility but face substantial hurdles in cost, safety, reliability, and governance.
