Prachi Garg

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

This paper proposes a self-supervised method that generates online interaction rollouts from the zero-shot VLA as additional training data for finetuning and demonstrates the success of this approach across test sets probing generalization on a real ALOHA robot and a new simulation benchmark in RoboTwin.