arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
arXiv:2609.38984v1 Announce Type: cross Abstract: World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and...
arXiv:2610.35044v1 Announce Type: new Abstract: World models are emerging as a fundamental component of policy learning for real-world agents, enabling agents to predict future world states and plan...
The paper introduces FRED, a framework that self‑distills future video representations for World Action Models (WAMs) by predicting these representations from noise using ground‑truth future observations as targets. By conditioning on historical observations, FRED improves the quality of future representations obtained in a single denoising step, thereby enhancing action prediction efficiency. Experiments on real‑world manipulation tasks show that FRED outperforms existing WAMs, especially on long‑horizon, out‑of‑domain, and zero‑shot scenarios, achieving a 50.0% success rate on tasks only seen in human videos versus 19.4% for the best competitor.
arXiv:2608. 06994v1 Announce Type: cross Abstract: World Action Models (WAMs) aim to construct a unified architecture capable of understanding world state evolution and guiding to generative motion planning.
arXiv:2610.28122v1 Announce Type: cross Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pre...
Rolling-WAM is a new formulation for World Action Models that spreads the joint video-action denoising process across multiple replanning cycles. It keeps a sliding window of video-action chunks at different noise levels, fully denoising the immediate chunk for execution while partially refining future chunks. This approach reduces latency, improves closed-loop responsiveness, and achieves a 4.5× speedup in steady-state replanning compared to standard WAMs while maintaining competitive manipulation performance.
TAPESTRY repurposes a pretrained video diffusion transformer into a robotic policy by treating robot actions as single-patch tokens in the model’s latent space, eliminating the need for video generation at inference. The lightweight linear projection maps each action timestep to the VAE patch dimensions, and the shared backbone is trained with three diffusion objectives—video diffusion, joint video+action diffusion, and action-only diffusion—to learn dynamics priors and action denoising. At test time, the model produces smooth action chunks in a single DDIM step, achieving strong performance on simulation and real‑robot benchmarks with significantly lower inference cost than comparable joint video–action policies.
CtrlWAM introduces a controllable world action model that jointly predicts actions (intent) and visual futures (foresight). By executing perturbed actions in a simulator and pairing them with noised visual outcomes, it aligns action predictions with their visual consequences, using warped video–action noise schedules to maintain visual layout responsiveness. The model extends beyond ego‑only control to multiple agent streams, improving action forecasts, video–action agreement, and command following in driving and robotics experiments.
arXiv:2606. 08962v1 Announce Type: new Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-modeling objective lets them learn from abundant unlabeled video rather than scarce labeled robot demonstrations.
arXiv:2606. 03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck.
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving policy model actively interacts with the environment, where its actions dynamically update the simulator state and directly influence the next set of generated sensor observations.