Sim-to-Real Data Collection for Robotics

Sim-to-real data collection closes the gap between simulation and the real world by capturing targeted real episodes in exactly the conditions simulation gets wrong, lighting, contact physics, wear, sensor noise, and human interference. Operant runs a gap analysis, prioritizes the highest-risk domains, and ties each capture target to a measurable evaluation slice. The goal is a policy that holds up after deployment, with improvement you can actually measure.

Where sim breaks

Strong simulation pipelines still miss the long tail: subtle contact dynamics, lighting and material variation, sensor noise, wear over time, and unpredictable human behavior. These are where deployed policies fail, and where targeted real-world capture pays off. Simulation tells you what should happen; real capture tells you what does.

Gap analysis framework

We start with a gap analysis workshop to identify where simulation coverage is thin or misleading, then produce a prioritized scenario list ranked by deployment risk. This keeps capture focused on the domains that move your metrics rather than collecting broadly and hoping.

Real-world slice design

Each prioritized gap becomes a capture slice with defined conditions, sensors, and acceptance criteria. Multimodal capture is handled through our multi-sensor synchronization service, and rare events through edge-case data collection.

Metrics to track

We define evaluation slices before capture and track policy performance on real held-out data before and after, so the value of new data is measurable. See sim-to-real metrics for how we frame this.

Example programs

A typical program pilots capture in the single highest-risk domain, validates the eval improvement, then scales across the prioritized list. This fits naturally into broader robotics data collection and applies directly to verticals like industrial manipulation.