Researchers led by Byeongguk Jeon introduce RoboWorld, an automated evaluation pipeline for generalist robot policies using neural video world models, submitted to ICML 2026.
The system achieves 0.989 Pearson correlation and 0.970 Spearman correlation with real-world robot evaluation, pairing a fast autoregressive video world model with a task-progress-aware vision-language model for policy scoring.
RoboWorld proposes Step Forcing, a technique combining anchored and one-step self-forwarded contexts to reduce train-test mismatch, enabling reliable long-horizon autoregressive rollouts while maintaining fast inference speed.