Paper Detail
Arnav Kumar Jain, Yilin Wu, Jesse Farebrother, Gokul Swamy, Andrea Bajcsy
The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limited real-world interaction. To unlock these downstream capabilities, a WM needs to jointly satisfy three desiderata: $\textit{(i)}$ fidelity (i.e., producing simulated trajectories that correlate with reality), $\textit{(ii)}$ consistency (i.e., producing simulated trajectories that are coherent over long horizons), and $\textit{(iii)}$ efficiency (i.e., producing simulated trajectories quickly). We propose $\texttt{WEAVER}$ (World Estimation Across Views for Embodied Reasoning): a WM architecture that simultaneously achieves all three desiderata, providing state-of-the-art results on robotic manipulation tasks. $\texttt{WEAVER}$ is a multi-view WM trained to predict future latents and reward values via a flow-matching loss. We distill the key design decisions across model architecture, memory, and prediction objectives required to unlock the kinds of long-horizon dynamic manipulation tasks that have confounded prior world modeling approaches. We apply $\texttt{WEAVER}$ in robotic hardware, demonstrating its effectiveness at policy evaluation ($ρ$=0.870 correlation with real-world success rate), policy improvement (real-world success rate improvement of $38\%$ on top of the $π_{0.5}$ robot foundation model), and test-time planning (real-world success rate improvement of $14\%$ with a $5-10\times$ speedup over prior WMs). $\texttt{WEAVER}$ also demonstrates better performance than prior WMs when evaluated on out-of-distribution scenarios. Code, models, and videos at: https://arnavkj1995.github.io/WEAVER/ .
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@article{jain2026texttt,
title = {\$\textbackslash{}texttt\{WEAVER\}\$, Better, Faster, Longer: An Effective World Model for Robotic Manipulation},
author = {Arnav Kumar Jain and Yilin Wu and Jesse Farebrother and Gokul Swamy and Andrea Bajcsy},
year = {2026},
abstract = {The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limited real-world interaction. To unlock these downstream capabilities, a WM needs to jointly satisfy three desiderata: \$\textbackslash{}textit\{(i)\}\$ fidelity (i.e., producing simulated trajectories that correlate with reality), \$\textbackslash{}textit\{(ii)\}\$ consistency (i.e., producing simulated trajectories that are coherent over long horizons), an},
url = {https://arxiv.org/abs/2606.13672},
keywords = {cs.RO},
eprint = {2606.13672},
archiveprefix = {arXiv},
}
{}