Pelican-Unified 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action

Summary

Pelican-Unified 1.0 trains one embodied model to reason in language, predict future video, and output robot action chunks from the same latent state. It targets the gap between perception-heavy VLMs, video world models, and executable robot policies.

Problem

Robot systems often split perception, reasoning, future prediction, and control into separate models, which can create mismatched states during long tasks.
VLA policies such as RT-2, OpenVLA, and π0 map observations to actions, but the excerpt says they usually lack explicit future video prediction.
Video world models can predict visual futures, but task reasoning and low-level robot actions may stay disconnected.

Approach

A Qwen3-VL 4B-based VLM reads observations, action history, and a language instruction, then writes a task- and action-oriented chain of thought.
The final hidden state of that reasoning trace is projected into a dense latent state z, which conditions both future video generation and action prediction.
A Wan2.2-5B-based diffusion transformer denoises future video latents and continuous action chunks in the same process, with separate input and output heads for each modality.
Training combines text loss, video flow-matching loss, and action SmoothL1 loss, and all three losses update the shared latent state.

Results

On 8 VLM benchmarks, Pelican-Unified scores 64.7 average, compared with 58.2 for Qwen3-VL-4B-Instruct and 27.5 for MolmoAct.
On embodied VLM benchmarks, it improves over Qwen3-VL-4B-Instruct by +28.2 points on Where2Place and +20.6 points on PhyX.
On RoboTwin 50-task dual-arm manipulation, it reaches 93.5% average success, with 93.6% in clean settings and 93.3% under randomized conditions.
On RoboTwin, it beats π0 at 62.2%, π0.5 at 79.8%, starVLA at 88.3%, LingBot-VA at 92.3%, and AIM at 93.1%; the excerpt says it is second-best among compared action methods.
On RoboTwin, 31 of 50 tasks reach at least 95% success, 39 tasks reach at least 90%, and 15 tasks reach 100%.
On WorldArena, it reports an EWM Score of 66.03 and ranks first among the compared world-model methods in the excerpt.

Link

https://arxiv.org/abs/2605.15153v1