What Is Axis Robotics
The Compounding Data Engine Accelerating Physical AI — contributed by everyone.
Teleoperate robots in simulation, take over from trained policies when they slip, and capture real-world interactions via mobile devices and specialized equipment. Every session produces data that trains tomorrow's robots. The simulation side runs entirely in your browser, with no hardware of your own.
Hardware and compute are ready; Physical AI is bottlenecked by data. Manual collection in closed labs is slow, expensive, and cannot produce the coverage real-world generalization needs.
Axis is the Compounding Data Engine Accelerating Physical AI, and it unifies the two most scalable data sources there are — high-fidelity simulation and ego-centric human data. Combining both pipelines gives the broadest environmental coverage available, compounding data diversity at foundation-model scale: every contribution improves the next policy, and each improved policy decides what data to collect next. Data stops being a one-time cost and starts compounding.
Scaling Physical AI for the Real World — Contributed by Everyone.
How It Works
Anyone with a browser can contribute — and every task on the platform moves through one lifecycle: Pre-training → Training → Post-training.
Pre-training: teach from scratch. You teleoperate a simulated robot arm to complete manipulation tasks — pick up a mug, sort objects, open a drawer. Each session produces a trajectory: a complete, timestamped recording of robot joint states, object poses, control actions, and task metadata. These demonstrations train a base policy for the task.
Post-training: correct the robot. Once a policy is trained, the task reopens in a new form. The policy attempts the task on its own while you watch; the moment it slips, you take over, fix the mistake, and hand control back. These corrections capture exactly what the policy cannot yet do — the highest-value data in robot learning — and train the next iteration of the policy.
Behind the scenes, every trajectory is verified by replay in simulation, cleaned, and expanded in an IsaacSim-based augmentation backend into thousands of photorealistic, domain-randomized training samples. These samples train the models and imitation learning policies that generalize across tasks, environments, and robot embodiments.
Data no longer flows one way. A trained policy's weaknesses determine which tasks open next; the corrections contributors provide train the next policy; and the improved policy dictates what new data to collect. One platform, one community — a continuously improving policy.
Every signed trajectory is assigned a unique Data ID and recorded on Base, and alongside it go the task it came from and the contributor who produced it — the three IDs together form a verifiable provenance trail.
Three Cornerstones
The same three reusable cornerstones power every part of the business, and they are what keeps data quality consistent across sources:
Universal Task Generation Engine — continuously streams diverse scenarios and comprehensive coverage of atomic skills to drive collection.
Distributed Global Workforce — a decentralized network that removes the physical and geographical limits of data gathering.
Unified Processing Pipeline — one standardized system that filters, augments, and transforms raw inputs into high-quality, golden datasets.
Three Products
Web
Simulation Data Platform — browser-based teleoperation, scaling high-fidelity simulation collection globally with no hardware constraints
10,000 valid trajectories per hour
Mobile
Ego-Centric Data Collection — capture real-world, first-person spatial data anywhere with just a smartphone, turning everyday human action into robot training sets
10,000+ valid hours per month
Infrastructure
Data-to-Model Pipeline — the central processing hub: unified processing layer, automated evaluation toolkits, and open-source datasets, from raw data to foundation models
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Full detail on all three, and the flywheel they sit in: The Compounding Data Engine.
Key Numbers
As of 10 October 2026
Global contributors
Trajectories collected
On-chain records
Published tasks
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The Team
Top AI and robotics researchers from Berkeley, CMU, UCLA, and NTU, alongside serial founders who have scaled consumer products past 30M+ users.
Building on another chain or shipping robots? See Solutions.
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