RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
This addresses the challenge of scalable, high-quality synthetic data generation for robot learning, though it is incremental by building on existing video generation and validation methods.
The paper tackles the problem of inconsistent action quality in synthetic robot data by introducing RoboCurate, a framework that filters actions via simulation replay, resulting in substantial improvements such as +70.1% success rate on GR-1 Tabletop and +179.9% on ALLEX humanoid manipulation.
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.