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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Neither synthetic data nor real-world data is always better for training physical AI. Simulation can produce varied examples quickly and safely; data collected on physical robots captures the actual hardware, sensors, contacts, and deployment conditions. In practice, teams often use simulation for breadth, transfer methods to reduce the mismatch, and real-world testing to find out whether a system works on its target robot and task.
What is the difference between synthetic and real-world data?
Synthetic data is generated in a computer simulation rather than collected directly from a physical robot. A simulation can vary scene details such as lighting, reflections, colors, object positions, camera setup, and selected physics parameters. It may also expose exact object poses or other ground-truth labels that are difficult to obtain from ordinary sensor recordings.
Real-world data comes from physical robots operating in actual environments. It reflects the particular robot’s dynamics, sensor noise, calibration, contacts, and surroundings—including effects that a simulator may not reproduce accurately. Those differences matter because a policy trained on data learns from the conditions represented in that data.
How do the two data sources compare?
| Training consideration | Synthetic or simulated data | Real-world data |
|---|---|---|
| Collection and iteration | Examples can be generated, reset, and procedurally varied in simulation; parallel environments can support rapid iteration. NVIDIA describes these advantages in its physical AI learning material. | Collection takes physical time, operator effort, and access to functioning hardware. NVIDIA identifies these as practical challenges of physical data collection. |
| Safety and failure cost | A simulated failure can usually be reset without damaging a physical robot. This makes simulation useful for exploring conditions that would be risky or costly to stage on hardware. Source: NVIDIA learning material. | Robot failures or unsafe exploration can damage equipment or create hazards, so data collection may need safeguards and supervision. Source: NVIDIA learning material. |
| Scenario coverage | Teams can deliberately vary scene appearance and selected physical parameters, including conditions that are difficult to stage repeatedly. Isaac Sim documentation describes synthetic-data generation and scene randomization. | Physical trials capture naturally occurring deployment conditions, including conditions not anticipated when simulation scenarios were designed. This follows from the documented mismatch between simulated and real systems. See the 2021 review of sim-to-real transfer. |
| Labels and sensor limits | Simulation may provide exact poses and ground-truth state or labels, subject to the simulator and scene assumptions. Source: NVIDIA learning material. | Measurements come through actual sensors, with their noise, occlusion, and calibration limitations. OpenAI discusses sensor noise and other sim-to-real differences in its 2018 work. |
| Transfer to a deployed robot | Success depends on whether the simulator and training variations represent the important conditions the robot will face. A model-based simulator is not a perfect copy of reality. Source: 2021 review. | Data directly reflects the physical domain, but gathering enough examples can be expensive and difficult to scale. Source: NVIDIA learning material. |
Can robots trained in simulation work in the real world?
Yes, it is possible—but it is not guaranteed. In a 2017 object-pushing study, OpenAI reported that a policy trained exclusively in simulation maintained similar performance on a real robot for that specific task. The result shows that simulation-only transfer can work in a particular setup; it does not establish that arbitrary simulated policies will work on other robots or tasks. Read the study’s sim-to-real transfer description.
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Whether a policy transfers depends on what the simulator captured, what varied during training, and how the real robot differs. Differences in dynamics, sensing, calibration, contact behavior, or deployment surroundings can make a policy fail even if it performed well in simulation. Real-world trials on the target hardware and task are therefore how a team determines whether its approach works in practice.
How do you close the sim-to-real gap?
The sim-to-real gap is the difference between what a robot experiences in simulation and what it experiences in physical operation. It cannot be removed by a single technique in every case. A practical approach combines broader simulation, transfer methods, and physical checks that reveal mismatches.
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1. Randomize conditions that may vary in deployment
Domain randomization varies simulated conditions during training so the policy encounters a broader range rather than relying on one narrowly configured scene. Possible variables include textures, lighting, camera position, friction, action delays, and sensor noise. The ranges should be chosen to cover relevant deployment conditions; randomization outside those ranges cannot ensure robustness to what the robot actually encounters.
NVIDIA describes domain randomization as a strategy that varies simulation parameters so a policy can become robust to values within the randomized range, including real-world values. See NVIDIA’s current domain-randomization course page.
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2. Vary simulated dynamics, not only appearance
Dynamics randomization changes plausible physical properties in simulation so the learned policy is exposed to differing robot or object behavior. This can help when real dynamics differ from the nominal simulated model. OpenAI’s 2017 object-pushing result is an example of this idea, but remains evidence for that experiment rather than a universal transfer guarantee. OpenAI’s study explains the method and task.
3. Train and evaluate with images and feedback where the task requires them
For tasks controlled from camera images, appearance and sensing differences can be central to transfer. Closed-loop control—where the system uses observations and adjusts actions as the task unfolds—can help it respond to changing conditions rather than rely only on a fixed action sequence. In OpenAI’s 2018 experiments, image-based learning was reported as about 5–10× slower and dynamics randomization as a 3× training slowdown compared with the authors’ reference setup. These are historical, study-specific comparisons, not current general performance estimates. Read OpenAI’s 2018 account.
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4. Calibrate, collect demonstrations, and test on physical hardware
Physical calibration and demonstrations can expose mismatches that a simulator did not model. NVIDIA’s Isaac Sim material describes collecting demonstrations in both simulation and the real world, and evaluating systems with software- or hardware-in-the-loop methods. See Isaac Sim’s documentation. A real-robot evaluation should use the target hardware and task conditions; strong simulated results alone do not establish deployment performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you use each type of data?
- Use simulation to expand breadth: generate varied scenes, repeat trials, reset after failures, and explore conditions that are hard or unsafe to stage physically.
- Use real-world data to ground the system: capture the actual robot, sensors, calibration, contact behavior, and environment.
- Use both when transfer is uncertain: train or develop in simulation, then use physical demonstrations, calibration, or evaluation to reveal gaps and guide revisions.
- Choose based on the task: compare collection effort, scenario coverage, label quality, domain match, safety, and validation needs rather than assuming one source is universally superior.
The choice is not simply “cheap synthetic data” versus “accurate real data.” Simulation offers control and scale but inherits modeling assumptions; physical data matches deployment more directly but can be harder to collect and scale. The right balance depends on which failure modes matter for the robot’s task and whether they can be represented and checked adequately.
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