Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance

Understand

Vision-Language-Action (VLA) models pre-trained on large, diverse datasets show remarkable potential for general-purpose robotic manipulation.

• However, a primary bottleneck remains in adapting these models to downstream tasks, especially when the robot's embodiment or the task itself differs from the pre-training data.
• This discrepancy leads to a significant mismatch in action distributions, demanding extensive data and compute for effective fine-tuning.
• To address this challenge, we introduce \textbf{Align-Then-stEer (\texttt{ATE})}, a novel, data-efficient, and plug-and-play adaptation framework.