Vision-Language-Action (VLA) models have emerged as a powerful paradigm for generalist robot manipulation, yet they lack geometric inductive biases: policies trained at specific orientations require substantially more data to generalize across rotational configurations. We present \textsc{EquiVLA}, the first general framework for end-to-end
SO(2)-equivariant VLA models, applicable to any architecture coupling a frozen vision-language backbone with a flow-matching Diffusion Transformer action head. \textsc{EquiVLA} introduces \textsc{EquiPerceptor}, which produces approximately
SO(2)-equivariant visual representations from frozen ViT features; and \textsc{EquiActor}, an exactly
SO(2)-equivariant flow-matching Diffusion Transformer action head. Together, they establish an approximate
SO(2) equivariance chain from camera observations to predicted action sequences. Instantiated on GR00T~N1.5 and evaluated across four LIBERO suites, CALVIN ABCD
→D, and five real-robot tasks on Mobile ALOHA, \textsc{EquiVLA} achieves
92.6% average success on LIBERO (vs.
78.1% baseline), an average sequence length of
4.03 on CALVIN (vs.
3.45), and improves real-robot success from
54% to
72%.