RoboOmni: Actions Are Just Another Modality for Vision-Language Models

Integrating Vision-Language Models (VLMs) into robotics has facilitated the development of generalizable Vision-Language Action (VLA) policies. However, unified discrete frameworks lag behind decoupled continuous designs due to limitations in action chunking and temporal modeling. To address this, we introduce RoboOmni, a unified multi-modal next-token prediction framework. Challenging the assumption that continuous modeling is essential for high-performance manipulation, RoboOmni demonstrates that actions are just another modality capable of being effectively modeled discretely. At the core of our method is Multi-Token Action Prediction (MTAP), which integrates action chunking directly into the discrete tokenizer. This design resolves temporal modeling bottlenecks and significantly reduces distribution shift between training and inference. By preserving the native VLM training and inference pipeline, RoboOmni naturally benefits from large-scale multimodal co-training and modern decoding optimizations. Extensive evaluations on the CALVIN, SimplerEnv, and real-world platforms confirm that RoboOmni establishes new state-of-the-art performance, significantly outperforming diffusion-based baselines such as $\pi_0$. Notably, combining our proposed MTAP with the FAST tokenizer achieves a 94.4% average success rate on CALVIN, while the Bin tokenizer implementation attains a 27$\times$ inference speedup compared to OpenVLA.