Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

Generalist robot policies must ground language in visual observations, anticipate how actions change the scene, and reason about task goals. Existing VLA approaches typically address these capabilities separately: VLM-based policies emphasize semantic grounding, video or world models emphasize visual dynamics, and goal-conditioned methods emphasize future-state reasoning. This separation can make policies brittle under instruction shift and environment or dynamics perturbations. We introduce Dynin-Robotics, an omnimodal masked-diffusion VLA model that represents robot trajectories as partially observed multimodal token sequences. A single denoising backbone is trained to unify action generation, future-state and world modeling, trajectory-to-language goal understanding, and instruction-conditioned goal-state prediction. At inference time, the same model performs policy generation through block-wise action denoising, with optional goal-state prediction, and world-action joint decoding. We first present diagnostic analyses showing that VLM-based and video/world-model-based policies exhibit complementary failure modes, motivating a unified formulation. Our experiments show that Dynin-Robotics achieves competitive standard-task performance on LIBERO, while improving robustness on instruction-shift and perturbation settings in LIBERO-Plus and VLABench.