Pixel Motion Diffusion is What We Need for Robot Control

We present DA WN (Diffusion is All We Need for robot control), a unified diffusion-based framework for language-conditioned robotic manipulation that bridges high-level motion intent and low-level robot action via structured pixel motion representation. In DA WN, both the high-level and low-level controllers are modeled as diffusion processes, yielding a fully trainable, end-to-end system with interpretable intermediate motion abstractions. DA WN achieves state-of-the-art results on the challenging CAL VIN benchmark, demonstrating strong multi-task performance, and further validates its effectiveness on MetaWorld. Despite the substantial domain gap between simulation and reality and limited real-world data, we demonstrate reliable real-world transfer with only minimal finetuning, illustrating the practical viability of diffusion-based motion abstractions for robotic control. Our results show the effectiveness of combining diffusion modeling with motion-centric representations as a strong baseline for scalable and robust robot learning. First, observations are encoded into conditional embeddings; Based on that, a latent diffusion Motion Director generates a pixel motion representation, which the diffusion policy Action Expert uses to create robot actions. Multi-stage pixel or point tracking based methods have recently emerged as a promising direction for robot manipulation, offering interpretable intermediate pixel motion and modular control (Y uan et al., 2024a; Gao et al., 2024; Xu et al., 2024; Bharadhwaj et al., 2024b;a; Ranasinghe et al., 2025). To address these limitations, we introduce a two-stage diffusion-based visuomotor framework in which both the high-level and low-level controllers are instantiated as diffusion models and glued by explicit pixel motions as illustrated in Figure 1. The high-level motion director, which is a latent diffusion module, takes current (multiview) visual observations and language instruction, and predicts desired dense pixel motion from a third-person view. This pixel motion could be regarded as a structured intermediate representation of desired scene dynamics to accomplish the language instruction.