Lack of high-quality data is the key barrier to effective deployment of robots in real-world environments. AIRA solves this by building a real-world simulation infrastructure for Physical AI. Unlike synthetic simulators, AIRA collects and processes real-world data from productive workspaces to create high-entropy simulation environments grounded in real tasks, layouts and sensor conditions.
AIRA has developed partnerships to survey 100s of real-world productive locations generating up-to-date, millimetre accurate, contextualised 3D maps and fully navigable visualisations. In addition to capturing static ‘point in time’ data, AIRA’s data acquisition process incorporates 3D time series data that records the activity taking place over time. This enables training Physical AI models with Reinforcement Learning and Imitation Learning.
“The principal challenge facing frontier robotics AI companies is building representative datasets. This means collecting data that captures the full range of situations a robot will face—especially infrequent, but mission-critical ones. Outside of robotics, AI models can adapt and generalize because we’ve already gone through the process of collecting internet-scale datasets, and curating them to span enough rare and everyday situations. Likewise, we need an abundance of realistic robotics data to build AI good enough to expand robotics markets.”
Terran Mott, Collossus Review, May 2025. Read more.
AIRA Tiered Simulation Structure
AIRA Training Grounds support Reinforcement Learning (RL), Imitation Learning (IL), and Teleoperation (TO), with policy training progressing from simple, structured tasks in clean simulations to complex, noisy, real-world conditions. Configurable domain randomisation and sensor disturbance layers enable robust fine-tuning and deployment readiness.
SANDPIT – Synthetic, Controlled.
Entry-level training environment featuring 100+ CAD-derived manufacturing and assembly scenes with minimal complexity. Labelled for task sequencing and robot positioning, with MES-based task flows and a basic simulation asset library. Ideal for early-stage RL policy design and validation.SIMPLE – Variation & Long-Tail Scenarios.
Introduces structured variation and stochastic events including domain randomisation, SideFX ‘Houdini’ based deformables, and long-tail placement, damaged goods, and human intrusions. Supports both RL and IL, including Teleop2SIM workflows.REALISTIC – Grounded in Real-World Perception.
Maintains prior geometry but adds perceptual realism using 3D point clouds and registered 2D/3D, 6DoF video from 50+ manufacturing sites. Tasks are based on actual operator behaviour, enabling robust policy training through vision-based RL and recorded-action IL.SUPER REALISTIC – Sensor & Environment Disturbance.
Final-stage validation of trained policies across 25+ physical production sites and 1,000+ tasks. Includes in situ execution with Time-of-Flight (ToF) camera tracking, action recording, and quality verification. Enables full assessment of deployment readiness and risk management in live industrial settings.REAL – Deployment Testing.
Final-stage validation of trained policies across 25+ physical production sites and 1,000+ tasks. Includes in situ execution with Time-of-Flight (ToF) camera tracking, action recording, and quality verification. Enables full assessment of deployment readiness in live industrial settings.
This structured progression supports curriculum-based learning strategies, improves sim-to-real generalisation, and enables safe, scalable deployment of humanoid policies in productive environments such as manufacturing.
AIRA enables physical AI system developers to move beyond lab demos and bring scalable value-add to real, productive manufacturing environments.
Training Subset: Manufacturing
We start with clean CAD based simulations of your workflow and progress to increasingly complex and noisy environments built from real-world data, ultimately enabling successful live deployment. Each Training Ground is structured to support progressive training and deployment of physical AI system control policies in real-world settings, such as various manufacturing feeder tasks. This staged progression supports robust policy learning and sim-to-real generalisation by gradually introducing complexity, variation, and noise.
The Manufacturing Training Ground toolset includes:
- 10,000+ digitised tasks from actual process flows.
- Spacially aligned 3D point cloud layouts.
- Registered 6 degree of freedom video matched to humanoid sensory models.
Contact us today on info@aira.online to discuss how we can integrate AIRA data and processes and bring the value of physical AI systems into your workflows.