Superpose Data Engine
Plain-English overview of Superpose's dataset optimization for robotics AI.
What we do
Superpose builds software that helps companies building humanoid robots and robotics foundation models create efficient, high-value datasets for training and evaluation.
How it works
The process begins with you defining what matters most for your model: edge cases you need to capture, safety criteria that must be met, diversity requirements across operational domains, or specific coverage targets.
We translate those priorities into a mathematical optimization problem, formulating constraints and objectives that reflect your real-world training goals and resource limitations.
Our optimization engine then solves for the highest-value data subset within your labeling and compute budgets, identifying which samples will contribute most to model performance.
The result - smaller datasets, faster iteration cycles, and stronger real-world performance.
What you bring
- •Raw or partially labeled data
- •Priorities (edge cases, safety, diversity)
- •Budget constraints (labeling, compute)
What we bring
- •Optimization algorithms
- •Automated subset selection
- •Integration with your pipeline
Integration
Our software integrates into your existing workflow with minimal overhead. No need to rebuild your infrastructure. We work with your current data formats and labeling tools.
FAQ
Do I need to change my data pipeline?
No. We integrate with your existing tools and formats. You keep your workflow; we optimize what goes into training.
How much data do I need to start?
We can work with datasets of any size, but the value increases with scale. If you have thousands of samples or more, we can show meaningful improvement.
What if my priorities change over time?
Our system is designed for iteration. As your priorities evolve (new edge cases, updated safety criteria), you can re‑run the optimization to get a fresh subset.