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Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation
This paper proposes Seed2Scale, a self-evolving data engine for embodied intelligence that uses very few human demonstrations to bootstrap automatic collection, automatic verification, and target policy learning. The…
embodied-aivision-language-actiondata-scalingself-evolving-datarobot-manipulationmultimodal-evaluation
Summary
This paper proposes Seed2Scale, a self-evolving data engine for embodied intelligence that uses very few human demonstrations to bootstrap automatic collection, automatic verification, and target policy learning. The core idea is to let a small model handle efficient exploration, a large model handle quality evaluation, and then use the filtered data to train the target VLA policy, thereby alleviating the robot data scarcity problem.
Problem
- Embodied AI / VLA models depend heavily on large-scale, high-quality expert demonstrations, but manual collection is expensive, creating a data bottleneck that limits the scaling of generalist robot policies.
- Existing automatic data generation methods either only perform local augmentation without active exploration, or are affected by the "embodiment gap," making it difficult to convert video knowledge into executable robot actions.
- More importantly, the signal-to-noise ratio of automatic sampling is low; if failed trajectories are mixed into training, errors will accumulate during self-iteration and lead to model collapse, so reliable automatic quality evaluation is needed.
Approach
- The paper proposes a heterogeneous collaborative framework: small-model collection, large-model evaluation, target-model learning. Put simply, a small and fast VLA tries many actions, a frozen VLM judges whether those attempts are good, and then the high-scoring data is used to train the final policy.
- It designs a lightweight collector, SuperTiny: ResNet-18 processes vision, T5-Small processes language, an MLP processes robot state, and then a lightweight Transformer decodes action chunks; time-weighted averaging makes control smoother and more stable, facilitating parallel rollout.
- It uses the pretrained Qwen3-VL-32B as the verifier, taking as input the task instruction, the current rollout video, and a reference successful video, and outputting a 0–10 quality score; only trajectories above a threshold are kept in the silver-label dataset, reducing contamination from failed data.
- Starting from only 4 seed demonstrations, it self-iteratively executes the closed loop of "train collector → generate trajectories in parallel → VLM scoring and filtering → expand dataset → train target model."
- The target model is SmolVLA, which learns a more robust action distribution from the filtered high-quality trajectories via conditional flow matching.
Results
- On 4 Agibot A2 manipulation tasks, using only 4 seed demonstrations per task, the average success rate increases from 22.18% to 68.57%, a relative improvement of 209.15%.
- Per-task results: Kitchen Cleanup 24.63% → 71.43% (+190.01%); Cup-to-Cup Transfer 23.50% → 64.14% (+172.94%); Can Stacking 7.50% → 65.90% (+778.67%); Air Fryer Manipulation 33.08% → 72.82% (+120.13%).
- Self-evolution scalability: on the hardest Can Stacking task, the target model's success rate shows a sustained upward trend across 8 self-evolution iterations; the paper emphasizes the stability of the trend, but the excerpt does not provide the specific value for each iteration.
- Compared with MimicGen on GR-1 tasks, Seed2Scale achieves an average policy success rate of 79.63% vs 36.00%; specifically, Cylinder Grasp 66.00% vs 37.25%, and Wheel Manipulation 93.25% vs 34.75%.
- For replay success rate, Seed2Scale averages 77.41% vs 34.75%; specifically, Cylinder Grasp 86.96% vs 21.00%, and Wheel Manipulation 67.86% vs 48.50%, indicating higher generated data quality.
- In trajectory quality, Seed2Scale approaches or even surpasses human demonstrations: Total Variation 1.34 (expert 1.32, MimicGen 3.68), Mean Absolute Jerk 0.0047 (expert 0.0063, MimicGen 0.0261), HF Ratio 0.30% (expert 0.22%, MimicGen 2.07%). In addition, SuperTiny has only 48M parameters, with inference at 38.08ms / 26.3 Hz, faster than ACT (45.67ms / 21.9 Hz) and Diffusion Policy (135.83ms / 7.4 Hz).
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