RoboCurate: Harnessing Diversity with Action-Verified Synthetic Data for Robot Learning
Overview
This research presents RoboCurate, a framework designed to improve robot learning by validating synthetic data. It addresses inconsistencies between generated videos and action labels by executing predicted actions in a simulator and verifying motion agreement. The method also expands scene diversity using generative models with structured editing axes and employs vision-language models to pre-filter physically implausible videos. Results show substantial relative improvements in success rates across various manipulation scenarios compared to using only real data.
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Environment: simulation
“themselves. To tackle this issue, we introduce RoboCurate, a novel framework for action-verified synthetic robot data that addresses inconsistencies between generated videos and annotated actions. Specifically, RoboCurate executes the actions predicted by an inverse dynamics model (IDM) in a simulator and verifies action consistency by measuring the motion agreement between the simulator rollout and the generated vi”
Exact source · revision 1Source owner not reported
Data origin: synthetic
“arXiv:2602.18742v2 Announce Type: replace Abstract: Synthetic data generated by video generative models has shown promise for robot learning as a scalable pipeline, but it often suffers from noisy action labels due to imperfect video generation or inaccurate action prediction. Recently, vision-language models (VLMs) have been leveraged to validate video quality, but they cannot directly evaluate the generated actions”
Exact source · revision 1Source owner not reported
Reported quantity · demonstrations: 300 other · Basis not reported
“red to using only real data, achieving +90.3% on GR-1 Tabletop (300 demos), +29.1% on DexMimicGen in the pre-training setup on simulation benchmarks, and +179.9% and +42.9% in the co-finetuning setup on challenging real-world ALLEX and OpenArm humanoid benchmarks, respectively.”
Exact source · revision 1Source owner not reported
Reported quantity · robots: 1 other · Basis not reported
“red to using only real data, achieving +90.3% on GR-1 Tabletop (300 demos), +29.1% on DexMimicGen in the pre-training setup on simulation benchmarks, and +179.9% and +42.9% in the co-finetuning setup on challenging real-world ALLEX and OpenArm humanoid benchmarks, respectively.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRoboCurate: Harnessing Diversity with Action-Verified Synthetic Data for Robot Learning
This research presents RoboCurate, a framework designed to improve robot learning by validating synthetic data. It addresses inconsistencies between generated videos and action labels by executing predicted actions in a simulator and verifying motion agreement. The method also expands scene diversity using generative models with structured editing axes and employs vision-language models to pre-filter physically implausible videos. Results show substantial relative improvements in success rates across various manipulation scenarios compared to using only real data.
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