Humanoid Training · Part of The Humanoid Group

Simulation & Sim-to-Real

Simulation lets a humanoid practise falls, steps and grasps at a scale no physical lab could match, without breaking anything. This guide covers why simulation matters, where the sim-to-real gap comes from and how teams narrow it.

By Arjun Rao · Updated October 2026

Why humanoids train in simulation

Physical robots are slow, costly and fragile teachers. Every fall risks damage, and every trial runs in real time. In simulation, many virtual copies of a robot can practise in parallel, faster than real time, with no wear on hardware. That makes simulation well suited to reinforcement learning, where a controller improves through trial and error against a reward. Locomotion is the classic example: walking, balancing and recovering from pushes are often learned largely in simulation before they reach a real robot. Simulation also lets teams rehearse rare or dangerous situations that would be impractical to stage on a real floor.

Where the sim-to-real gap comes from

No simulator matches reality exactly. Friction, motor behaviour, sensor noise, cable drag, latency and the way objects deform are all approximations. A controller that exploits those small errors can look perfect in simulation and stumble on its first real step. This mismatch is known as the sim-to-real gap. It tends to be widest for contact-rich tasks, such as grasping soft or irregular objects, and narrower for well-modelled motion, such as walking on flat floors. Knowing which of your tasks fall on which side helps set realistic expectations for how much a simulator can achieve on its own.

How teams narrow the gap

Common techniques include domain randomisation, where physical properties, lighting and textures are varied during training so the controller learns to cope with uncertainty; system identification, where the simulator is tuned using measurements from the real robot; and fine-tuning on a smaller set of real-world data after simulated training. Many teams combine all three. When you evaluate a platform, ask which simulators it supports, whether an accurate model of the robot is provided, and how controllers trained in simulation are deployed to the hardware. Good answers here can spare your team a great deal of integration work.

Sources and further reading

Robot Learning From Randomized Simulations: A Review — Frontiers in Robotics and AI.
Peer-reviewed review of sim-to-real work: why the reality gap appears and how randomising the simulator helps policies transfer.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World — OpenAI and UC Berkeley (arXiv; IROS 2017).
Early domain-randomisation study: varied rendering lets a network trained only on synthetic images guide real-world grasping.
Learning agile and dynamic motor skills for legged robots — ETH Zurich (arXiv; published in Science Robotics).
ETH Zurich study that trains legged-robot policies in simulation and moves them to the ANYmal robot, fall recovery included.

Common questions

Can a humanoid be trained entirely in simulation?

Some skills, especially locomotion, are learned largely in simulation. Most teams still validate and fine-tune on real hardware before relying on a skill in practice.

What is domain randomisation?

A technique that varies physical properties, lighting and textures during simulated training, so the resulting controller copes better with the variation of the real world.

Learning From Demonstration — How humanoids learn skills from human demonstrations and teleoperation data.
Simulation & Sim-to-Real — Why humanoids train in simulation first, and what it takes to cross the gap to the real world.
Evaluation & Readiness — How to judge whether a humanoid is safe, reliable and ready for real work.
Preparing Your Team — The roles, skills and habits your organisation needs before humanoids arrive.
Humanoid Training Services — When to outsource robot data collection, teleoperation and evaluation, and what to ask a provider.
How to Train a Humanoid — A plain-language walkthrough of teaching a humanoid a new task, from demonstration to testing.
Teleoperation for Data — VR, leader-arm and exoskeleton rigs for recording humanoid demonstrations, and what makes a run worth keeping.
Learning to Walk — How reinforcement learning teaches humanoids to walk and balance in simulation before they step out for real.
Training Data and Privacy — Recording people at work or at home to train robots: consent, data protection law and careful data handling.
Choosing First Tasks — How to pick a humanoid's first skills, set pass marks and budget for the demonstrations each one needs.
Safety While Training — Zones, stops and supervision that keep people safe while a humanoid is being taught, tested and debugged.
Latest updates — Teaching robots now takes data operations, simulation and careful testing. These figures and dated updates show where training data comes from, how fast robots learn new tasks and how results are checked.
About us — What we cover, how we source every figure, and who publishes this site.

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