Pelican-Unified 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action
2026-05-14 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors introduce Pelican-Unified 1.0, a model that combines understanding, reasoning, imagining the future, and taking actions all in one system. It uses a single vision-language model to process visual scenes, instructions, and past actions into a common understanding, and then predicts future videos and actions together. Unlike training separate models for each task, Pelican-Unified 1.0 learns everything jointly, improving overall performance. Their tests show that combining these abilities in one model does not weaken its skills but instead achieves strong results across multiple benchmarks.
embodied foundation modelvision-language model (VLM)unified representationautoregressive reasoninglatent variableUnified Future Generatorvideo generationaction predictionmulti-task learningbenchmark evaluation
Authors
Yi Zhang, Yinda Chen, Che Liu, Zeyuan Ding, Jin Xu, Shilong Zou, Junwei Liao, Jiayu Hu, Xiancong Ren, Xiaopeng Zhang, Yechi Liu, Haoyuan Shi, Zecong Tang, Haosong Sun, Renwen Cui, Kuishu Wu, Wenhai Liu, Yang Xu, Yingji Zhang, Yidong Wang, Senkang Hu, Jinpeng Lu, Nga Teng Chan, Yechen Wu, Yong Dai, Jian Tang, Xiaozhu Ju
Abstract
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.