Find where a policy fails. Fix it. Measure again.
Evaluation and feedback systems designed to identify policy failures, collect corrective data, and support iterative model updates.
One loop, from failure to fix.
Every failure found becomes the next thing the model learns, and the next run is measured against the same test.
- 01EvaluateRun the policy on held-out tasks and scenes, with every trace recorded.
- 02Find failuresGroup the failures by cause, not just count them, so the gap is specific.
- 03Collect correctionsTurn each gap into a targeted data campaign: corrections and recoveries for exactly those cases.
- 04UpdateTrain on the new data, then evaluate again against the same test.
What you can use.
Corrective data collection
Status: PilotContributors step in where a policy fails in simulation and demonstrate the correction, paired with the failure it fixes.
Private evaluation
Status: In developmentYour policy run on held-out tasks or on scenes built for your task, with success rates, traces, clustered failures and a regression report.
Build with Robotensor
We collaborate with robotics companies, research labs, and industrial partners on robot learning, simulation, data collection, and evaluation.
Contact us to discuss your robotics applications, data requirements, or research challenges.