Fine-Tuning VLAs with Self-Demonstrated Generative Control for Multi-Task Manipulation

2026-08-19

AI Summary

为解决VLA模型在新机器人上性能下降问题，提出一种自监督方法生成在线交互数据进行微调，以保持原有指令跟随能力并学习新技能。

Abstract

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 strong multi-task policies that, on the target robot, (1) inherit prior tasks distilled from the zero-shot model, (2) enable generalist instruction following, while (3) learning new skills from expert data with improved sample efficiency. We demonstrate the success of our approach across test sets probing generalization on a real ALOHA robot and a new simulation benchmark in RoboTwin. Video results are available at https://self-supervised-control.pages.dev/

Problem

• VLA
• finetuning
• robot

Innovation

• self-supervised method
• online interaction rollouts
• finetuning VLA
• multi-task policies
• generalist instruction following