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Data & Simulation

Robot models are only as capable as the data behind them, and physical data is slow and costly to collect. Makers and labs combine real demonstrations, simulation and video to close the gap.

By Arjun Rao · Updated October 2026

Learning from demonstration

Much robot training data comes from people showing robots what to do. In teleoperation, an operator controls the robot remotely, using a headset, motion-capture suit, handheld controllers or a matched leader arm, while the robot records what it sees and how it moves. These demonstrations are valuable because they come from the robot's own body, but collecting them is slow and needs skilled operators. That is why the scale and variety of a maker's data effort is a useful question for buyers.

Simulation and synthetic data

Simulation lets robots practise an enormous number of attempts in virtual worlds, far faster and more safely than in reality. It is especially useful for locomotion, balance and reinforcement learning, where a policy improves by trial and error. The catch is the gap between simulation and reality: friction, contact, lighting and sensor noise never match perfectly. Techniques such as randomising physics and visuals during training help policies cope when they move to real hardware, but real-world testing remains essential.

Video, fleets and the data flywheel

Some approaches also learn from ordinary video of people doing everyday tasks, extracting useful knowledge about objects and motion even without robot action data. Once robots are deployed, their own operation can generate more data, which in turn improves the model, an idea often called a data flywheel. For buyers this raises practical questions: what data does the robot collect on your site, where is it stored, who can use it for training, and can you opt out?

Sources and further reading

Open X-Embodiment: Robotic Learning Datasets and RT-X Models — Open X-Embodiment Collaboration (arXiv).
Pools data from 22 robot types across 21 institutions and shows one model can carry skills between different robots.
R3M: A Universal Visual Representation for Robot Manipulation — Stanford University and Meta AI (arXiv; CoRL 2022).
Pretrains a visual model on everyday human video and shows it helps robots learn manipulation from fewer demonstrations.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization — UC Berkeley and OpenAI (arXiv; ICRA 2018).
Randomising physics during simulated training lets a policy run on a real robot with no real-world training data.

Common questions

Why does robot data matter to me as a buyer?

It shapes how well a robot handles your objects and environment, and data collected on your premises may raise privacy and confidentiality questions worth settling in your contract.

What is the sim-to-real gap?

The difference between how a robot behaves in simulation and in the real world. Small mismatches in physics or sensing can make a policy that works virtually stumble on real hardware.

Robot Foundation Models — Large models trained on broad data, then adapted to control robots across many tasks.
Vision-Language-Action Models — Models that see a scene, read an instruction and output the robot's next movements.
Data & Simulation — Teleoperation, demonstrations and simulated worlds: where robot training data comes from.
Evaluating Humanoid AI — How to judge a humanoid's intelligence on your own tasks, not in a demo video.
LLMs as Task Planners — How large language models break a spoken request into steps a robot can actually carry out.
World Models for Robots — Learned simulators and video prediction that let robots imagine the result of an action before they act.
On-Device or Cloud AI — Where a robot's AI runs, and how latency, privacy, connectivity and power shape that choice.
Open-Source Robot Models — Open robot foundation models and datasets: what gets released, what open really means and how to use them.
AI Regulation for Robots — How the EU AI Act, NIST's AI Risk Management Framework and India's AI guidelines apply to robots.
Latest updates — Robot models are now tested in unseen homes and judged on safety, yet benchmarks still show wide gaps. These figures and dated updates show what progressed, what failed and how to read the claims.
About us — What we cover, how we source every figure, and who publishes this site.

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