# What Is the Reality Gap?[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#what-is-the-reality-gap 'Link to this heading')

## Source Domain vs Target Domain[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#source-domain-vs-target-domain 'Link to this heading')

In the research community, we often talk about two domains:

1. The **source domain** (simulation)

2. The **target domain** (real world)

These domains represent all the conditions a robot might encounter, including terrain, motor parameters, and other factors that define its operational environment.

As you might imagine, there is a big mismatch between simulation and reality. This is what we refer to as the **reality gap**.

So where does this gap come from? We can boil it down to three main factors: approximation errors, model errors, and unmodelled dynamics.

### Approximation Errors[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#approximation-errors 'Link to this heading')

Approximation errors arise from the inherent limitations of simulations in replicating real-world physics:

- Physics and rendering simulations are approximations of the underlying laws of nature.

- Discretization in simulations can lead to non-physical behaviors, such as object penetration.

- Simulations may not conserve fundamental physical principles like momentum.

### Model Errors[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#model-errors 'Link to this heading')

Model errors occur due to inaccuracies in representing the physical robot and its environment:

- Link lengths, body masses, and friction coefficients are never 100% accurate in simulations.

- Manufacturing tolerances and wear and tear contribute to discrepancies between the simulated and real robot.

### Unmodelled Dynamics[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#unmodelled-dynamics 'Link to this heading')

There will often be parts of the dynamics which you don’t model at all:

- Complex action mechanisms, such as series elastic actuators or springs, are often simplified.

- Interactions involving suction cups or other complex contact scenarios are challenging to model precisely.

- Sensor noise and actuator inaccuracies are often simplified or overlooked in simulations.

- System delays, including network latency and actuator response times, are not always accounted for in simulations.

Diagram illustrating the complexity of a series elastic actuator.[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/transfer-policies-from-simulation-to-reality/02-the-reality-gap/index.html.md#id1 'Link to this image')

Addressing these factors is crucial for successful sim-to-real transfer. Researchers employ various techniques such as domain randomization, real-to-sim transfer, and behavior regularization to bridge the reality gap and improve the performance of policies transferred from simulation to real-world robots.