Study Finds Demonstration Curation Metrics Don't Reliably Improve Robot Learning Policies
Researchers found that metrics used to filter defective robot demonstration data are poorly correlated with the quality of the resulting trained policy, with the best-detecting metric producing the worst-performing robot. The study used a contact-rich pick-and-place benchmark with a controlled defect—early gripper release—and tested seven curation metrics. The findings suggest the field needs to shift evaluation standards from defect detection accuracy to downstream policy performance.
A preprint from arXiv examines whether demonstration-curation metrics that identify flawed training episodes in robot learning actually lead to better behavior-cloning policies when used to filter training data. Testing on the LIBERO pick-and-place benchmark with a deliberately introduced structural defect (early gripper release during the carry phase), the authors found a sharp decoupling between detection performance and policy quality: the metric with the highest defect-detection AUROC (0.804) yielded only 13.3% task success, while a metric with a much lower AUROC (0.638) produced a policy achieving 90.0% success—nearly matching the 93.3% oracle trained on clean ground-truth data. The contaminated, uncurated baseline succeeded on just 3.3% of rollouts. A key methodological finding is that five of the seven evaluated metrics exploit episode length as a proxy for defect labels, artificially inflating AUROC scores in a way that disappears once episode length is controlled. The authors argue that curation benchmarks must control for episode length and that curation methods should ultimately be judged by the policies they produce, not by detection metrics alone. The testbed, metric implementations, and evaluation pipeline have been released publicly.
What's missing
The study uses a single, controlled defect type (early gripper release) on one benchmark (LIBERO pick-and-place), leaving open whether the decoupling between detection accuracy and policy performance generalizes to other defect types, tasks, or robot learning paradigms. The paper does not evaluate whether combining multiple curation metrics or using ensemble approaches could reconcile detection and policy performance. As a preprint, the work has not yet undergone peer review.
What different sources said
- arXiv cs.LGCenter
What Demonstration Curation Metrics Do to Your Policy
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