Open‑HumanScale presents the first fully open recipe for scaling egocentric human video pretraining to 50,000 hours, supported by the curated Open‑HumanScale‑50K corpus annotated with hand motion and natural language. Using this corpus, the authors develop HumanWAM‑Vanilla, a world–action model that achieves high success rates on RoboTwin 2.0 and RoboDojo after robot post‑training, and HumanWAM‑Goal, a visual‑goal‑conditioned model that further improves zero‑shot performance and demonstrates embodied in‑context learning on physical robots. The paper also releases the dataset, curation pipelines, pretrained checkpoints, and evaluation tools to enable reproducible scaling research in embodied AI.
arXiv:2611.06817v2 Announce Type: replace Abstract: Egocentric human video offers a scalable source of manipulation priors for robot learning, yet translating raw data scale into generalizable robot...
Embodied foundation models are expected to benefit from data scaling like large language models, but face a much tighter data bottleneck. Teleoperated real-robot trajectories remain the dominant pretraining source due to their precise action supervision and embodiment alignment, yet their scalability is limited by high collection cost, acquisition difficulty, and low behavioral and environmental diversity.
InternW0-Δ is a unified World Action Model that integrates pretrained visual dynamics, scene semantics, 4D geometry, and motion priors within a Mixture-of-Transformers framework to generate robot actions. It leverages a frozen VLM for semantic guidance, a 4D foundation model for geometric priors, and introduces Causal Imprint to learn future-relevant scene changes without future-video rollout. The model is pretrained on a newly curated 20K‑hour heterogeneous corpus of robot and human demonstrations, achieving superior performance on simulation benchmarks and real‑robot platforms.
arXiv:2604.19734v2 Announce Type: replace-cross Abstract: Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alt...
arXiv:2606. 17846v1 Announce Type: cross Abstract: Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale.
arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.
ZimaBlue is a scalable framework that learns generalizable World Action Models (WAMs) from large-scale egocentric videos. It follows a three-stage curriculum: causal video pre‑training, video‑action mid‑training with a unified action representation, and final specialization to a target robot. The system employs an asynchronous Slow‑Fast architecture to enable real‑time 30 Hz action prediction, achieving a jump in real‑robot zero‑shot success from 36.1% to 77.8% when leveraging over 120,000 hours of embodied video.
arXiv:2610.33685v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are bottlenecked by the scarcity of robot data. Egocentric human manipulation videos offer an abundant, diverse,...
arXiv:2609.25627v1 Announce Type: cross Abstract: General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate prec...
Zero-WAM introduces a causal video-action model that enables robots to perform unseen manipulation tasks by following in-context human video guidance. The authors create HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks, and propose an in-context future chunk prediction objective to prevent shortcut learning. In simulation, Zero-WAM attains a 47.0% success rate on seven unseen tasks, outperforming the best video-action baseline by 29.5 percentage points, and demonstrates real‑world generalization to complex, long‑horizon, and fine‑grained tasks.
AtomEgo investigates how to integrate large-scale egocentric human interaction data into embodied foundation model pre‑training. The study uses a curated 2,659‑hour corpus and a scalable data pipeline to evaluate three co‑training paradigms across vision‑language‑action and world‑action architectures. Results show that the benefit of egocentric data depends on both its scale and the quality of alignment with robotic embodiment, offering practical guidance for scalable ego‑robot pre‑training.
Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often...