Teleoperation vs. Simulation: Where Should Your Robot Training Data Actually Come From?

Robot training faces a data bottleneck because physical interaction data must be deliberately generated through real-world collection or simulation rather than drawn from an internet-scale archive. Teleoperation provides highly faithful demonstrations from the actual robot and environment, making it especially valuable for contact-rich tasks such as grasping, assembly, and insertion, but it is expensive, slow, and limited by operator availability and fatigue. Simulation can generate thousands of inexpensive, safe, and diverse episodes quickly, supporting pre-training, coarse movement learning, and rare or hazardous edge-case exploration, yet its simplified physics, visuals, and actuator behavior create a sim-to-real gap that is most severe for deformable materials and precise contact tasks. Effective datasets depend less on raw volume than on integrity, representativeness, complete sensor records, balanced variations, and accurate annotation of events, outcomes, and unusual cases. The recommended approach is a hybrid pipeline in which teleoperation supplies real-world grounding, simulation expands coverage and scale, and emerging world models synthesize predictive experience, with consistent curation and annotation making data from all sources usable for robot policy training.

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