Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

Robot world models must predict not merely plausible videos, but outcomes faithful to specified actions. Across heterogeneous robots, numerical configurations preserve joint-level detail but leave visual consequences implicit, whereas projected visual actions expose motion geometry but obscure depth and configuration information. Our key idea is to let these complementary representations interact throughout generation. We introduce Pelican-Sim 1.0, a shared action-conditioned world model simulator that injects numerical configurations and camera-aligned whole-arm skeleton videos into alternating video-transformer blocks. Each condition shapes hidden states already informed by the other, coupling configuration detail with projected geometry. Sparse mixture-of-experts layers accommodate heterogeneous dynamics, while causal adaptation and few-step distillation support efficient autoregressive rollouts. Trained on approximately one million real and simulated trajectories, Pelican-Sim 1.0 outperforms all evaluated baselines on five video-quality metrics across held-out splits of AgiBotWorld Beta, RoboMIND, and RoboTwin. Ablations demonstrate improvements over single-condition, concatenation, and late-fusion alternatives, supporting the importance of how complementary conditions interact. On RoboTwin, generated-data augmentation raises policy success from 70.0% to 93.0%, while imagined policy optimization with task-specific simulator adaptation improves success from 62.7% to 75.4%. These results support interleaved numerical–visual conditioning as an effective design for shared robot world models and demonstrate their utility for policy learning. We will release model checkpoints and code.