Robot task evaluation
Classify an attempt as success, failure, or unclear from visible evidence.
For research teamsHomeMove research faster.
Turn one narrow annotation backlog into a trained, quality-controlled student volunteer workflow. Your team defines the task and keeps final approval.
Discuss a pilotObservable work, not expert judgment.
A student can answer, “Did the robot place the object inside the container?” They should not be asked why a control policy became unstable.
We scope tasks where a correct answer can be learned from visible evidence, examples, and a clear rubric. Technical interpretation stays with the research team.
Tasks students can complete.
Each workflow is trained from examples, bounded by a written rubric, and reviewed before your team uses the result.
Failure identification
Mark the first visible failure and select from researcher-defined categories.
Action segmentation
Confirm or correct timestamps for grasping, moving, placing, or releasing.
Label verification
Approve, correct, or escalate labels generated by an automated system.
Model comparison
Compare two outputs against a specific, observable evaluation rubric.
Vision annotation
Label clearly visible objects, interactions, boxes, masks, or keypoints.
How we check quality.
- 01Task-specific training and qualification
- 02Hidden benchmarks and consistency checks
- 03Independent annotations when agreement matters
- 04Annotator accuracy tracking and retraining
- 05Escalation for unclear examples
- 06Researcher review and final approval
What a pilot measures.
- ■Annotation accuracy
- ■Training time
- ■Annotator agreement
- ■Acceptance rate
- ■Researcher review time
- ■Total time saved
Bring us one careful pilot.
Start with public or appropriately prepared data, a narrow rubric, and a result we can measure.