Real-world variation
Changing light, foliage occlusion and uneven terrain combine with differences in ripeness, shape and growing position to challenge perception and manipulation.
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DATA FOR REAL-WORLD WORK / 07
Agricultural scenarios
Real-world learning, from field to harvest.
Build skills in crop inspection, harvesting, produce handling and packing under changing outdoor conditions.
Sample access depends on the selected dataset. Use a published sample link when available, or request a sample for evaluation.
The scenario
Real-world learning, from field to harvest.
Agricultural scenarios take place in greenhouses, open fields, orchards and initial produce-processing areas. They document complete workflows in which robots locate a crop, approach the plant, move foliage aside, harvest produce and place it into collection containers. Inspection, sampling, grading and packing extend the training context beyond picking alone.
Operating environments
Greenhouses, open fields, orchards and initial produce-processing areas with plants at varied positions and growth stages.
Tasks to learn
- 01Inspect crops
- 02Harvest fruit and vegetables
- 03Move foliage aside
- 04Collect samples
- 05Grade produce
- 06Pack transport containers
Data and signals
Data may include crop appearance, spatial location, end-effector movements, contact forces and produce condition after harvesting.
Capabilities to develop
Support gentle grasping, handling occlusions and adaptation to outdoor conditions, connecting target detection with harvesting and container placement.
See the work in context.
Dedicated spaces for scene photography and task videos. Published media will show the environment and the actions captured.
Scenario image
Task demonstration video
Agriculture
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Sample access depends on the selected dataset. Use a published sample link when available, or request a sample for evaluation.