RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory 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 inconsistent action quality due to imperfectly generated videos. Recently, vision-language models (VLMs) have been leveraged to validate video quality, but they have limitations in distinguishing physically accurate videos and, even then, cannot directly evaluate the generated actions themselves. To tackle this issue, we introduce RoboCurate, a novel synthetic robot data generation framework that evaluates and filters the quality of annotated actions by comparing them with simulation replay. Specifically, RoboCurate replays the predicted actions in a simulator and assesses action quality by measuring the consistency of motion between the simulator rollout and the generated video. In addition, we unlock observation diversity beyond the available dataset via image-to-image editing and apply action-preserving video-to-video transfer to further augment appearance. We observe RoboCurate's generated data yield substantial relative improvements in success rates compared to using real data only, achieving +70.1% on GR-1 Tabletop (300 demos), +16.1% on DexMimicGen in the pre-training setup, and +179.9% in the challenging real-world ALLEX humanoid dexterous manipulation setting.
699d154f4e37ec6dfa1bc51b
Seungku Kim
699d154f4e37ec6dfa1bc51c
Suhyeok Jang
claimed_verified
2026-02-25T17:32:02.933Z
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Suhyeok Jang
glory-hyeok
user
glory-hyeok
699d154f4e37ec6dfa1bc51d
Byungjun Yoon
claimed_verified
2026-03-06T13:57:33.729Z
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Byungjun Yoon
happyhappy-jun
user
happyhappy-jun
699d154f4e37ec6dfa1bc51e
Dongyoung Kim
699d154f4e37ec6dfa1bc51f
John Won
699d154f4e37ec6dfa1bc520
Jinwoo Shin
RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory 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 inconsistent action quality due to imperfectly generated videos. Recently, vision-language models (VLMs) have been leveraged to validate video quality, but they have limitations in distinguishing physically accurate videos and, even then, cannot directly evaluate the generated actions themselves. To tackle this issue, we introduce RoboCurate, a novel synthetic robot data generation framework that evaluates and filters the quality of annotated actions by comparing them with simulation replay. Specifically, RoboCurate replays the predicted actions in a simulator and assesses action quality by measuring the consistency of motion between the simulator rollout and the generated video. In addition, we unlock observation diversity beyond the available dataset via image-to-image editing and apply action-preserving video-to-video transfer to further augment appearance. We observe RoboCurate's generated data yield substantial relative improvements in success rates compared to using real data only, achieving +70.1% on GR-1 Tabletop (300 demos), +16.1% on DexMimicGen in the pre-training setup, and +179.9% in the challenging real-world ALLEX humanoid dexterous manipulation setting.
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency and augmenting observation diversity via image editing and video transfer techniques.
video generative models vision-language models synthetic data generation action quality evaluation simulator replay image-to-image editing video-to-video transfer robot learning observation diversity