具身智能研究者终于有了一个统一框架——Pelican-Unified 1.0 用一个模型搞定理解、推理、想象和行动，不再需要拼凑三个独立系统。做机器人、仿真或多模态模型的团队值得关注，它证明了统一不意味着妥协。
Pelican-Unified 1.0 是首个按照统一原则训练的具身基础模型，将场景理解、指令推理、未来想象和动作执行整合到单一模型中。它使用一个视觉语言模型（VLM）作为统一的理解和推理模块，并通过统一未来生成器（UFG）同时生成未来视频和动作。实验表明，统一并未牺牲性能：在八个VLM基准上平均得分64.7，在WorldArena上排名第一（66.03），在RoboTwin上达到93.5（动作方法中第二好）。该工作展示了统一范式在保持专家级性能的同时，将多种能力融合到一个模型中的可行性。
Pelican-Unified 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action
We present Pelican-Unified 1.0, the first embodied foundation model trained according to the principle of unification. Pelican-Unified 1.0 uses a single VLM as a unified understanding module, mapping scenes, instructions, visual contexts, and action histories into a shared semantic space. The same VLM also serves as a unified reasoning module, autoregressively producing task-, action-, and future-oriented chains of thought in a single forward pass and projecting the final hidden state into a dense latent variable. A Unified Future Generator (UFG) then conditions on this latent variable and jointly generates future videos and future actions through two modality-specific output heads within the same denoising process. The language, video, and action losses are all backpropagated into the shared representation, enabling the model to jointly optimize understanding, reasoning, imagination, and action during training, rather than training three isolated expert systems. Experiments demonstrate that unification does not imply compromise. With a single checkpoint, Pelican-Unified 1.0 achieves strong performance across all three capabilities: 64.7 on eight VLM benchmarks, the best among comparable-scale models; 66.03 on WorldArena, ranking first; and 93.5 on RoboTwin, the second-best average among compared action methods. These results show that the unified paradigm succeeds in preserving specialist strength while bringing understanding, reasoning, imagination, and action into one model.