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[2602.18742] RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
Summary
The paper presents RoboCurate, a framework for generating synthetic robot data that enhances action quality through simulation replay and diverse observation techniques.
Why It Matters
RoboCurate addresses the challenges of inconsistent action quality in robot learning by leveraging synthetic data and simulation. This innovation can significantly improve the training of robots, making them more effective in real-world applications, which is crucial as robotics technology continues to advance.
Key Takeaways
- RoboCurate improves synthetic data generation for robot learning.
- The framework evaluates action quality by comparing simulated actions with generated videos.
- It enhances observation diversity through image editing and video transfer techniques.
- Substantial improvements in success rates were observed in various robotic tasks.
- The approach offers a scalable solution for training robots effectively.
Computer Science > Robotics arXiv:2602.18742 (cs) [Submitted on 21 Feb 2026] Title:RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning Authors:Seungku Kim, Suhyeok Jang, Byungjun Yoon, Dongyoung Kim, John Won, Jinwoo Shin View a PDF of the paper titled RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning, by Seungku Kim and 5 other authors View PDF HTML (experimental) 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 gene...
