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Robotics company LiberAI announced Liber-0 on its official X account and technical blog. It is a World Action Model that jointly predicts future frames and actions, pretrained on more than 2.2 million hours of human experience and anchored by precise hand-motion data from its own LiberSense Glove. On 60 tasks proposed by GPT-6 across 600 trials, Liber-0 reached 36.0% overall success without robot demonstrations collected for those tasks, but only 10.5% on challenging multi-step tasks. No open weights or API have been announced.

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On October 19 Dream Labs came out of stealth with a $90M seed round and opened a public API for Dream-1. Dream-1 is a World Action Model (WAM) that learns to predict video and actions together. The company says it can zero-shot control bimanual parallel-gripper robots without any demonstrations from that robot, and it ran 27 unseen robots in simulation, including the 18-year-old PR2. Pricing is $3/hour for Dream-1 and $2/hour for Dream-1-flash. In an independent Robocurve evaluation, Dream-1 scored 59 on RoboDojo-RC Tier 1 versus 48 for the previous best. The company stresses it is a research beta not validated for safety-critical use.

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Pixel-space world models for GUI agents require predicting future screen images prior to action selection. However, full-screen pixel reconstruction suffers from extreme latency and frequently triggers identity shortcuts, where generated screens merely copy the past rather than reflecting action-induced mutations. Researchers from KAIST introduced the first Latent World Action Model for GUI agents, shifting predictive objectives from pixel generation to action-conditioned transitions in latent space. Featuring shared multimodal representations with decoupled action and transition heads alongside action-aware contrastive losses, the framework achieves up to a 40.6% relative gain in online task success while reducing inference latency by 98.9%.

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World Action Models (WAMs) represent a foundational robotic paradigm by predicting future visual dynamics and Cartesian actions directly from initial observations. However, existing WAMs struggle with long-horizon execution: synthesizing dense video rollouts incurs prohibitive computational overhead and temporal drift, while predicting single terminal frames deprives the policy of progressive visual guidance. Meta FAIR and Shanghai Jiao Tong University introduce ProWAM (arXiv:2610.02508), an efficient architecture that jointly predicts actions and an ordered sequence of sparse visual sub-goals. Sub-goal prediction scales naturally by learning from large-scale action-free video datasets, decoupling complex visual foresight from low-level policy control. ProWAM executes a single video-backbone forward pass to cache sparse sub-goal features, enabling rapid closed-loop replanning through lightweight action denoising without full video re-generation. Evaluated across extensive robotic benchmarks, ProWAM sets SOTA records on LIBERO-Plus (85.8%, +35.9% relative gain) and randomized RoboTwin (75.7%). In real-world zero-shot physical manipulator trials, ProWAM achieves a 70.0% success rate in unseen environments, outpacing the strongest baseline by +15.0 percentage points (+27.3% relative gain).

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Shanghai AI Lab and OpenDataLab open-sourced InternW0-Δ, a unified World Action Model (WAM) pretrained on over 20,000 hours of curated robotic demonstrations, UMI data, and egocentric videos. Built on a Mixture-of-Transformers (MoT) architecture that binds visual predictive dynamics with robot actions, it introduces 'Causal Imprint' to inject future representations directly into the action expert without requiring costly future-video rollouts during inference.

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Embodied robotics researchers have unveiled DeltaWAM (arXiv: 2609.28811, GitHub: AIGeeksGroup/DeltaWAM), introducing Delta World Action Models and Streaming Delta Memory (SDM) for bimanual manipulation. While prior world-action models adapt heavy video generative backbones to predict full dense future frames—wasting compute on static visual backgrounds and creating catastrophic inference latency—DeltaWAM predicts compact visual deltas alongside actions. On the rigorous RoboTwin benchmark, DeltaWAM with SDM elevates clean task success to 85.4% and randomized visual success to 83.9% while cutting per-step inference latency by 36.57%.

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