EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models

• URL: http://arxiv.org/abs/2606.19784v1
• Date: Thu, 18 Jun 2026 04:36:57 GMT
• Title: EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models
• Abstract summary: We present textscEquiVLA, the first general framework for end-to-end $mathrmSO(2)$-equivariant VLA models.<n>textscEquiVLA is applicable to any architecture coupling a frozen vision-language backbone with a flow-matching Diffusion Transformer action head.
• Score: 7.807191061951358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\mathrm{SO}(2)$-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 $\mathrm{SO}(2)$-equivariant visual representations from frozen ViT features; and \textsc{EquiActor}, an exactly $\mathrm{SO}(2)$-equivariant flow-matching Diffusion Transformer action head. Together, they establish an approximate $\mathrm{SO}(2)$ equivariance chain from camera observations to predicted action sequences. Instantiated on GR00T~N1.5 and evaluated across four LIBERO suites, CALVIN ABCD$\to$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 $72\%$.

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