7 hours of human data beats 17 hours of public datasets at 85% robot task success
YuXiang_IRVL · x · 2026-09-16
- Researchers benchmarked 7 hours of Human Archive hand-pose data against 4 widely used public datasets (HOT3D, H2O, HOI4D, TACO totaling 17 hours) by training robot manipulation policies.
- Results: Human Archive alone hit 85% task success / 65.6% progress; the public datasets only 57.5% / 52.5%; mixing all five 60% / 56.3%.
- The authors argue training a policy is a better way to evaluate hand-pose dataset quality than eyeballing poses, and welcome further comparisons.
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