Optimize robotics data for better real‑world performance
We help companies building humanoid robots and robotics foundation models optimize their training data. Better coverage, smarter selection, faster iteration.
The Challenge
Training data bottlenecks in robotics AI development
Building general-purpose robots and robotics foundation models requires massive amounts of diverse, high-quality training data. Yet as datasets scale, a fundamental challenge emerges: not all data is equally valuable.
Training runs often include significant data overlap, with similar scenarios captured repeatedly, while critical edge cases and challenging failure modes remain underrepresented. Traditional manual curation becomes impractical at scale, creating a bottleneck between data collection and model improvement.
The result is longer iteration cycles and higher costs as teams navigate the tradeoff between dataset size and training efficiency, often without clear visibility into which data points will drive the most performance gains.
Our Approach
Intelligent data selection through optimization
Superpose treats dataset curation as an optimization problem. You define what matters for your specific use case: whether that's maximizing coverage of edge cases, ensuring safety-critical scenarios are well-represented, or balancing diversity across operational domains.
Our software analyzes your full dataset and formulates the selection problem mathematically, accounting for your training objectives, budget constraints, and compute resources. The optimization engine then identifies the subset of data that will deliver the highest marginal value for your next training run.
Instead of manually reviewing thousands of trajectories or training on redundant examples, you get a curated dataset optimized for your goals. This enables faster iteration cycles and more efficient use of labeling and compute budgets.