Whitepaper

The Data Problem in Robotics

Why do some robots generalize to new tasks with surprisingly little data while others struggle after thousands of demonstrations?

Because the bottleneck isn’t data volume—it’s data diversity. Once a robot has mastered common scenarios, collecting more of the same adds little value. The biggest gains come from identifying the experiences the model hasn’t seen yet and curating data that fills those gaps.

The best robotics teams aren’t racing to collect the largest datasets. They’re analyzing failures, finding the missing scenarios hidden inside existing robot logs, and building targeted data curation workflows that improve generalization instead of simply increasing dataset size.

This whitepaper explains why diversity consistently outperforms volume in modern robotics—and how physical AI teams can build better datasets without collecting more of the same.

Get practical insights, including:

Why robot data is fundamentally different: Why robotics can’t rely on the web-scale data strategy that powered LLMs and vision models
Why diversity beats volume: Research showing that broader experience—not more demonstrations—drives generalization
When more data makes models worse: How negative transfer from mismatched datasets can reduce performance
Why failure analysis should guide data collection: How identifying failure modes leads to smarter curation than blind data gathering
What video, synthetic data, and dataset pooling can—and can’t—solve: Where today’s shortcuts help and where real robot data is still essential
How to find the data that matters: Using multimodal search and embeddings to turn one failure into a targeted data curation workflow across an entire robotics corpus