Synthetic Data vs Real Data: Building Better Robotics Training Pipelines

Not entirely. Synthetic data can provide substantial training coverage for some tasks. Real-world data remains essential for capturing physical conditions, uncovering unexpected failures, and validating deployment performance.

There is no universal number. It depends on the task, hardware, environment, and model. Focus on whether the data covers the situations where the robot currently struggles.

It is the difference between performance in simulation and on physical hardware, often caused by differences in sensing, dynamics, actuation, timing, and environmental conditions. Test transfer on physical hardware; visual realism alone does not establish that a simulated task behaves correctly. 

It is the difference between a robot’s performance in simulation and on physical hardware, often caused by differences in sensing, dynamics, actuation, timing, and environmental conditions. To assess whether a policy will work in the real world, we test it on physical hardware across representative conditions and edge cases, and then compare the results with its performance in simulation. This is because visual realism alone does not guarantee reliable real-world performance. 

Physical collection should complement simulation when teams need to validate simulated assumptions, expand coverage of unfamiliar conditions, or investigate failures observed in deployment. Real-world data helps establish which scenarios matter, while simulation can generate controlled variations around them. Using both allows teams to test transfer to physical hardware and improve training coverage without relying on either source alone. 

Compare its scenarios, labels, and model performance against carefully selected real-world data. Check whether it represents the conditions and failure modes relevant to deployment.