iFlyBot-VLA Technical Report
Authors: name: Yuan Zhang, name: Chenyu Xue, name: Wenjie Xu, name: Chao Ji, name: Jiajia wu, name: Jia Pan

We introduce iFlyBot-VLA, a large-scale Vision-Language-Action (VLA) model
trained under a novel framework. The main contributions are listed as follows:
(1) a latent action model thoroughly trained on large-scale human and robotic
manipulation videos; (2) a dual-level action representation framework that
jointly supervises both the Vision-Language Model (VLM) and the action expert
during training; (3) a mixed training strategy that combines robot trajectory
data with general QA and spatial QA datasets, effectively enhancing the 3D
perceptual and reasoning capabilities of the VLM backbone. Specifically, the
VLM is trained to predict two complementary forms of actions: latent actions,
derived from our latent action model pretrained on cross-embodiment
manipulation data, which capture implicit high-level intentions; and structured
discrete action tokens, obtained through frequency-domain transformations of
continuous control signals, which encode explicit low-level dynamics. This dual
supervision aligns the representation spaces of language, vision, and action,
enabling the VLM to directly contribute to action generation. Experimental
results on the LIBERO Franka benchmark demonstrate the superiority of our
frame-work, while real-world evaluations further show that iFlyBot-VLA achieves
competitive success rates across diverse and challenging manipulation tasks.
Furthermore, we plan to open-source a portion of our self-constructed dataset
to support future research in the community