RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning

TL;DR

RoboCurate introduces a novel framework for robot data generation that evaluates action quality through simulation replay, improving robot learning success rates by ensuring physically accurate and diverse training data.

Contribution

The paper presents RoboCurate, a new method that filters and augments synthetic robot data by verifying actions with simulation and enhancing diversity through image editing and video transfer.

Findings

Significant success rate improvements on GR-1 Tabletop and DexMimicGen benchmarks.

Large gains in real-world humanoid manipulation tasks.

Effective validation of action quality via simulation replay.

Abstract

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.