Robotics Foundation Models Market Size & Share Analysis - Growth Trends and Forecast (2026 - 2031)

Global Robotics Foundation Models Market Trends and Insights

Industrial Automation and Labor Shortages

Manufacturing and logistics employers are increasing automation investment because shortages now affect tasks that fixed equipment cannot easily perform. U.S. industrial robot installations rose 11% to 38,000 units in 2025, while robot density reached 307 operational units per 10,000 manufacturing employees.^{[1]International Federation of Robotics, “US Robot Industry Returns to Double Digit Growth,” IFR Press Release, ifr.org} These conditions favor robots that can interpret changing product layouts, package shapes, and work instructions. Foundation models can support restocking, mixed-item handling, and other tasks where conventional robots require extensive reprogramming. Global industrial robot installations reached 621,000 units in 2025, and Asia accounted for 79% of installations, showing that the automation push extends beyond North America. The robotics foundation models market therefore benefits when employers seek flexible capacity rather than another narrowly programmed machine that must be reconfigured when local tasks, stock profiles, or product conditions change.

Demand for General-Purpose Robot Intelligence

Enterprises with task-specific robots often incur new integration costs when product lines or facility layouts change. This issue has increased interest in systems that can transfer learned skills across tasks and robot types. Physical Intelligence reported that its π0.7 model combined skills learned from separate datasets to complete novel manipulation sequences without task-specific training.^{[2]Physical Intelligence, “π0.7: A Steerable Generalist Robotic Foundation Model with Emergent Capabilities,” arXiv, arxiv.org} The result points to a practical value proposition for general-purpose robot intelligence, particularly in automotive, electronics, and logistics operations with frequent variation. A broader robot policy may reduce the need to maintain separate systems for each handling or assembly task. This opportunity supports the robotics foundation models market because enterprises increasingly view model access and ongoing updates as core automation infrastructure for facilities where changing tasks otherwise increase integration effort and delay operational returns.

Expansion of Multimodal Vision-Language-Action Models

Vision-Language-Action, or VLA, models combine visual inputs, language instructions, and robot-state information within one control system. This can reduce the integration burden associated with separate perception, planning, and action components. NVIDIA stated that GR00T N1.7 was trained using 32,000 hours of real demonstration and egocentric human data, along with 8,000 hours of simulated rollouts. A common model can be post-trained for material handling, packaging, and inspection, reducing the number of configurations an enterprise needs to maintain. In healthcare, NVIDIA-Medtech reported that GR00T-H-N1.7 achieved 25% full end-to-end suturing success on the SutureBot benchmark. These developments broaden the robotics foundation models market beyond traditional industrial automation while raising the importance of safety assessment in settings where robot behavior affects workers, patients, and customers.

Open Robotics Models Lowering Development Barriers

Open-source and open-weight models are changing the cost structure of robot software development. OpenVLA used 970,000 robot episodes from the Open X-Embodiment dataset and outperformed RT-2-X by 16.5% in absolute task success across 29 evaluation tasks while using fewer parameters. This evidence indicates that robotics performance can improve through better training data and model design rather than parameter count alone. Xiaomi released Xiaomi-Robotics-0 with weights, inference code, and evaluation tools, then released post-training code for real-robot deployment. As access to baseline models improves, integrators can concentrate on deployment, data collection, and customer-specific validation. The robotics foundation models market may therefore see faster participation by mid-sized integrators, although model providers must protect performance advantages through data and operating experience accumulated across real customer environments and varied physical conditions.

High Cost and Scarcity of Real-World Robot Data

Real-world robot demonstrations require synchronized visual, force, motion, and language data, which is costly to collect at a commercial scale. The constraint is most evident in garment handling, surgical work, and complex assembly, where physical variation limits the value of synthetic data alone. NVIDIA-Medtech released Open-H-Embodiment in 2026 with 770 hours of surgical robot data from 50 or more institutions across 20 robot platforms.^{[3]NVIDIA-Medtech, “Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics,” arXiv, arxiv.org} The scale of this coordinated dataset also shows the operational work needed to standardize actions and data streams across institutions. Universal Robots and Scale AI introduced UR AI Trainer to capture synchronized motion, force, and vision data from production robots for industrial VLA training. The robotics foundation models market remains advantaged toward companies with deployed fleets, even as simulation, cross-embodiment training, and shared datasets improve access for firms without large operating fleets or specialist data teams.

Safety Certification and Liability Uncertainty

Safety rules for learning-based robot behavior have not progressed as quickly as model deployment. ISO 10218-1:2025 and ISO 10218-2:2025 set safety requirements for industrial robots, including requirements relevant to design and use. ANSI/A3 R15.06-2025 also updated industrial robot safety requirements for functional safety, risk assessment, end effectors, and cybersecurity. These standards are important, but do not establish complete evaluation methods for neural-network action policies that encounter unseen conditions. Responsibility may remain unclear among the model developer, robot manufacturer, system integrator, and operator if a robot causes damage or injury. This uncertainty can delay use in healthcare, defense, and public spaces, limiting near-term demand where operators need predictable evidence before allowing robots to work alongside people in the robotics foundation models market.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Model Architecture: VLA Models Lead While Reasoning Architectures Extend Capability

Vision-Language-Action models held 54.67% of the robotics foundation models market share in 2025. Their lead reflects the ability to process images, language task descriptions, and robot-state data in one backbone. This design supports instruction-following behavior without complex links between separate perception, planning, and action components. NVIDIA described GR00T N1.7 as an open, commercially licensed VLA for general humanoid robot skills and identified training partners, including Unitree Robotics and Agile Robots.^{[4]NVIDIA-Medtech, “GR00T-H-N1.7,” Hugging Face, huggingface.co} The industrial installed base gives VLA models a clear commercial route across established industrial settings with repeatable handling and assembly work in the robotics foundation models market.

World models hold a smaller revenue position but remain important because they represent physical environments for planning and testing. NVIDIA-Medtech stated that Cosmos-H-Surgical-Simulator generated realistic surgical video from robot kinematics across nine surgical platforms, which can support validation before physical deployment. Embodied reasoning models are projected to expand at a 46.53% CAGR through 2031, the fastest rate within the architecture segment. Physical Intelligence reported that π0.7 matched task-specific systems on coffee preparation, laundry folding, and box assembly without task-specific training data. Behavior policy, cross-embodiment control, and tool-orchestration models serve narrower needs as development frameworks improve access for smaller integrators that need affordable starting points and adaptable control policies in the robotics foundation models market.

By Deployment Mode: Cloud Systems Support Continuous Model Improvement

Cloud-based deployment accounted for 57.26% of revenue in 2025. Enterprises use cloud systems for centralized model updates, shared compute, and the aggregation of robot data across sites. Universal Robots and Scale AI stated that UR AI Trainer captures production motion, force, and vision data to support imitation learning from the lab to the factory. This model connects deployment data with policy refinement and lowers infrastructure barriers for mid-sized enterprises. The cloud channel is projected to expand at a 43.61% CAGR through 2031, making it the fastest deployment mode in the robotics foundation models market.

On-premises deployment remains important where operations need data control, low latency, or reliable local operation. Defense sites, pharmaceutical cleanrooms, and remote mining operations may not accept dependence on external cloud connections. NVIDIA positioned Jetson Thor for real-time robot inference and control at the edge, helping facilities retain sensitive operational data. European data-residency rules and sector security requirements also support local deployment where cloud use is restricted. The coexistence of cloud learning and edge control will remain relevant as customers balance model improvement with operational control, local resilience, and data-protection obligations in the robotics foundation models market.

By Application: Warehouse Use Leads While Home Service Use Accelerates

Warehouse picking and sorting held 23.16% of revenue in 2025. Warehouses require robots to handle varied stock-keeping units, irregular packages, mixed-weight totes, and changing storage patterns. These tasks are poorly suited to rigid automation because they require contextual recognition and flexible grasping. Foundation models can reduce manual reprogramming when packaging or product mixes change. The continued expansion of e-commerce and distribution operations gives the robotics foundation models market a dependable commercial base in warehouse automation.

Industrial assembly, material handling, packaging, and mobile inspection also remain meaningful application areas. Model-guided systems can adjust to new part shapes and production changes that previously required manual intervention. Mobile inspection and navigation can reduce human exposure in energy and manufacturing sites with hazardous conditions. Home service execution is projected to expand at a 46.32% CAGR through 2031, supported by lower humanoid hardware costs and stronger general-purpose model capability. Unstructured homes remain harder than factories because robots must work around vulnerable occupants, diverse objects, and limited machine-readable structure, so widespread use remains a longer-term commercial opportunity for the robotics foundation models market as reliability and safety expectations remain demanding.

By End User: Manufacturers Provide Volume While Healthcare Providers Advance Fastest

Manufacturers held 35.21% of revenue in 2025 and remained the largest end-user group. Automotive producers, electronics assemblers, and food processors need adaptable automation because product variation and shorter production runs make fixed programming less efficient. Foundation models offer greater flexibility, not just lower labor costs, across changing operating conditions. Commercial deployments of humanoid and general-purpose systems show that these technologies are moving beyond pilot settings in manufacturing and logistics. Logistics and warehousing providers represent the next major user group because distribution work is labor-intensive and service targets are strict, creating demand for systems that can adjust to changing workloads without lengthy reprogramming cycles.

Healthcare providers are projected to expand at a 47.27% CAGR through 2031, the fastest end-user rate. Surgical robotics and hospital logistics can extend clinical capacity and improve operational workflows where safety and validation requirements are met. NVIDIA-Medtech presented GR00T-H-N1.7 as a commercially licensed model for surgical and healthcare robotics using data from 50 or more institutions and 20 robot platforms. The company reported 25% full end-to-end suturing success on the SutureBot benchmark. Clinical validation and medical-device compliance will influence adoption timing, particularly in European surgical settings, where deployment must align with clinical validation and established medical-device processes in the robotics foundation models market.

Geography Analysis

North America held 45.74% of the robotics foundation models market share in 2025. The region combines frontier model developers, cloud infrastructure, and enterprise spending on automation. U.S. industrial robot installations increased by 11% to 38,000 units in 2025, while robot density reached 307 units per 10,000 manufacturing employees, supporting deployments in manufacturing, logistics, and related services. ANSI/A3 R15.06-2025 sets updated industrial safety requirements that align with ISO 10218 and create a clearer validation reference for deployers. 

Asia-Pacific is projected to expand at a 46.84% CAGR through 2031, representing the fastest-growing regional opportunity. The Japan Robot Industry Association reported that 2025 robot orders rose 25.7% to JPY 1,045.6 billion (USD 6.97 billion), and forecast 2026 orders of JPY 1,220 billion (USD 8.13 billion).^{[5]Japan Robot Industry Association, “Statistical Press Release: 2025 Robot Orders and Production,” Japan Robot Industry Association, jara.jp} Japan is coordinating data collection and shared development of foundation models through the AI Robot Foundation Technology Consortium. South Korea had a robot density of 1,220 units per 10,000 manufacturing employees, creating a large installed base for retrofits in electronics and semiconductor production. Asia-Pacific can expand the robotics foundation models market through new deployments and upgrades to established automated facilities.

Europe had a robot density of 267 units per 10,000 manufacturing employees in 2024, the highest regional level reported by the International Federation of Robotics. This installed automation base supports adoption, although detailed compliance requirements and slower investment in frontier models may limit the pace relative to North America and Asia-Pacific. South America, the Middle East, and Africa held a modest but emerging position in the robotics foundation models market. Brazil offers demand from automotive assembly and food processing, while South Africa offers a relevant use case in mining inspection and hazardous navigation. Broader adoption will depend on cloud infrastructure, smart manufacturing, and logistics initiatives in Gulf countries, as well as localized data that reflects regional languages, work environments, and operating conditions in the robotics foundation models market.