Fine-Tuning VLAs with Self-Demonstrated Generative Control for Multi-Task Manipulation
State-of-the-art vision-language-action (VLA) models such as $π_{0.5}$ exhibit strong semantic understanding, instruction following and task behavior. However, when deployed on new robots, even minor mismatches in hardware configuration relative to pretraining can cause severe performance drops. Finetuning the VLA on in-domain expert data from the new embodiment improves performance on the expert task but leads to a loss in its original instruction following and behavioral priors. In this paper, we propose a self-supervised method that generates online interaction rollouts from the zero-shot VLA as additional training data for finetuning. Our experiments show this finetuning scheme yields...
| Authors | Prachi Garg, Steve Xing, Prahit Yaugand et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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