3D-VLA: A 3D Vision-Language-Action Generative World Model
Haoyu Zhen Xiaowen Qiu Peihao Chen Jincheng Yang Xin Yan Yilun Du Yining Hong Chuang Gan
Recent vision-language-action (VLA) models rely on 2D inputs, lacking
integration with the broader realm of the 3D physical world. Furthermore, they
perform action prediction by learning a direct mapping from perception to
action, neglecting the vast dynamics of the world and the relations between
actions and dynamics. In contrast, human beings are endowed with world models
that depict imagination about future scenarios to plan actions accordingly. To
this end, we propose 3D-VLA by introducing a new family of embodied foundation
models that seamlessly link 3D perception, reasoning, and action through a
generative world model. Specifically, 3D-VLA is built on top of a 3D-based
large language model (LLM), and a set of interaction tokens is introduced to
engage with the embodied environment. Furthermore, to inject generation
abilities into the model, we train a series of embodied diffusion models and
align them into the LLM for predicting the goal images and point clouds. To
train our 3D-VLA, we curate a large-scale 3D embodied instruction dataset by
extracting vast 3D-related information from existing robotics datasets. Our
experiments on held-in datasets demonstrate that 3D-VLA significantly improves
the reasoning, multimodal generation, and planning capabilities in embodied
environments, showcasing its potential in real-world applications.