arXiv Computer Vision
Open-HumanWAM: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models
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Open-HumanScale: An Open Recipe for Scaling Human and Robot Experience for Embodied Foundation Models
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.
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HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining
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.
arXiv Computer Vision
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InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
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 AI
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UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
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 Machine Learning
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Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
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 Computer Vision
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ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
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.