Chapter 2: Data

Core Questions

• How do we collect diverse, high-quality robotics data at scale?
• What annotation strategies work for multi-modal robotic datasets?
• How do we ensure data quality and avoid distribution drift?

Topics

2.1 Data Collection Strategies

• Teleoperation vs. autonomous collection
• Multi-robot data aggregation
• Simulation-to-real transfer
• Sensor calibration and synchronization

2.2 Annotation and Labeling

• Human-in-the-loop annotation
• Foundation model-based auto-labeling
• Quality assurance and validation
• The cost-quality trade-off

2.3 Dataset Curation

• Diversity metrics and coverage analysis
• Handling edge cases and long-tail scenarios
• Data augmentation for robotics
• Privacy and safety considerations

2.4 Benchmark Datasets

• Standard evaluation datasets
• Domain-specific benchmarks
• Transfer learning across datasets
• The reproducibility crisis