← 研踪 PaperTrailarXiv:2602.18742 · v2
RoboCurate: Harnessing Diversity with Action-Verified Synthetic Data for Robot Learning
首次提交 2026-02-21 15:33
版本更新 2026-11-05 17:30
雷达观测 2026-11-06 12:09
摘要
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 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 video. To better leverage such a framework, we further expand the scene diversity of synthetic data beyond the available dataset through off-the-shelf generative models with structured editing axes. We then use a VLM to pre-filter physically implausible generated videos before verifying their action labels. Across diverse manipulation scenarios, RoboCurate yields substantial relative improvements in success rates compared 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.
作者备注：CoRL 2026 G2A Workshop Accepted (Oral); Project page is available at https://seungkukim.github.io/robocurate/
观测到的版本
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v2 · 2026-11-05 17:30 · 最新观测版本
RoboCurate: Harnessing Diversity with Action-Verified Synthetic Data for Robot Learning
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 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 video. To better leverage such a framework, we further expand the scene diversity of synthetic data beyond the available dataset through off-the-shelf generative models with structured editing axes. We then use a VLM to pre-filter physically implausible generated videos before verifying their action labels. Across diverse manipulation scenarios, RoboCurate yields substantial relative improvements in success rates compared 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.
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