Features
Human-generated computer-use demonstrations
Reinforcement learning environments for GUI agents
Multi-step task data (form filling, navigation, software operation)
Web browser and desktop application interaction data
Custom dataset options available
Scalable data collection infrastructure
Data samples for evaluation and benchmarking
Focus on realistic UI interaction trajectories
Solar-powered rover with controlled illumination
Per-plant high-resolution imaging across growing season
AI-powered pest, weed, and disease detection per plant
Real-time plant growth monitoring
Automated yield forecasting from imagery and environmental data
Crop phenotyping via computer vision (leaf size, biomass)
Quality inspection for fruits and vegetables
Food waste reduction analytics
Integration of satellite, weather, and soil data layers
Multi-angle image capture with controlled rolling speeds
Machine learning to identify plant-environment interaction patterns
Data collection in muddy fields and bright sunlight
Terabyte-scale image datasets per field
Controlled rolling speeds and sophisticated sensors
