ROBOTICS DATA CURATION
In DevelopmentDataset optimization for robotics foundation models
We're building optimization-driven tools to help teams curate robotics training datasets. The goal: reduce redundancy, improve coverage, and accelerate model iteration—validated in your training and evaluation loop.
What We're Building
Import and manage robotics learning datasets across sources, with structure detection, split tracking, and safe retries at scale.
Compute dataset representations and diagnostics to surface redundancy, coverage gaps, and long-tail scenarios—and enable fast similarity search.
Generate candidate training subsets using constraint-aware optimization under user-defined objectives (coverage/diversity) and budgets, with versioned outputs and lineage.
HOW IT WORKS
From raw data to a curated training set
Superpose helps teams turn large robotics datasets into smaller, versioned training sets—aligned with objectives and budgets.
Connect
Point Superpose at your dataset and metadata (tasks, splits, constraints).
Profile
Generate dataset diagnostics to surface redundancy and coverage gaps.
Select
Propose candidate subsets aligned with your objectives and budgets.
Export
Export a versioned dataset and run report for reproducible training and evaluation.
Connect
Point Superpose at your dataset and metadata (tasks, splits, constraints).
Profile
Generate dataset diagnostics to surface redundancy and coverage gaps.
Select
Propose candidate subsets aligned with your objectives and budgets.
Export
Export a versioned dataset and run report for reproducible training and evaluation.
APPROACH
Mathematical optimization, not manual heuristics
Traditional dataset curation relies on heuristics and manual selection. We frame curation as a constrained selection problem: balance coverage and diversity with dataset size, under task-specific constraints and budgets—then produce candidate subsets with diagnostics for evaluation.
CAPABILITIES
End-to-end dataset curation workflow
WHAT YOU ACHIEVE
More signal, less data waste
Superpose helps robotics teams reduce redundancy and target underrepresented scenarios—so you can improve model outcomes and iterate faster, often with fewer training examples, depending on the task and dataset.
Coverage Visibility
Surface redundancy and coverage gaps across your dataset—so you can prioritize what to collect, label, or replay.
Data Efficiency
Focus training on higher-signal examples. In many settings, teams may reach target performance with fewer training samples by removing low-value repeats and emphasizing gaps.
Targeted Performance
Prioritize the scenarios that matter most (edge cases, long-tail regions, safety-relevant conditions) to improve reliability where your evaluation measures it.
Reproducibility
Every run produces versioned outputs with lineage and metadata—making experiments repeatable, auditable, and easy to compare across iterations.
Interested in learning more?
We're currently in active development. Reach out to discuss how we can help optimize your robotics training data.