Latent Action Pretraining from Videos
Authors: name: Seonghyeon Ye, name: Joel Jang, name: Byeongguk Jeon, name: Sejune Joo, name: Jianwei Yang, name: Baolin Peng, name: Ajay Mandlekar, name: Reuben Tan, name: Yu-Wei Chao, name: Bill Yuchen Lin, name: Lars Liden, name: Kimin Lee, name: Jianfeng Gao, name: Luke Zettlemoyer, name: Dieter Fox, name: Minjoon Seo

We introduce Latent Action Pretraining for general Action models (LAPA), an
unsupervised method for pretraining Vision-Language-Action (VLA) models without
ground-truth robot action labels. Existing Vision-Language-Action models
require action labels typically collected by human teleoperators during
pretraining, which significantly limits possible data sources and scale. In
this work, we propose a method to learn from internet-scale videos that do not
have robot action labels. We first train an action quantization model
leveraging VQ-VAE-based objective to learn discrete latent actions between
image frames, then pretrain a latent VLA model to predict these latent actions
from observations and task descriptions, and finally finetune the VLA on
small-scale robot manipulation data to map from latent to robot actions.
Experimental results demonstrate that our method significantly outperforms
existing techniques that train robot manipulation policies from large-scale
videos. Furthermore, it outperforms the state-of-the-art VLA model trained with
robotic action labels on real-world manipulation tasks that require language
conditioning, generalization to unseen objects, and semantic generalization to
unseen instructions. Training only on human manipulation videos also shows
positive transfer, opening up the potential for leveraging web-scale data for
robotics foundation model.