亿级短视频数据突破具身智能Scaling Law！Being-H0提出VLA训练新范式

Core Viewpoint - The article discusses the advancements in embodied intelligence, particularly focusing on the development of the Being-H0 model, which utilizes human hand movement data to enhance robot action capabilities and address the data scarcity issue in visual-language-action (VLA) models [1][30]. Group 1: Data Scarcity and Solutions - The lack of real-world data is hindering the development of VLA models, with existing data falling short by three orders of magnitude compared to the required scale of over one hundred million training samples [2]. - The research team from Peking University and BeingBeyond proposed a solution by creating a large-scale dataset from human operation videos, achieving a dataset size in the hundreds of millions [3][17]. Group 2: Being-H0 Model and Innovations - Being-H0 is the first large-scale pre-trained VLA model based on human video hand data, utilizing a novel "physical instruction tuning" framework to map human hand movements to robot action spaces [5][10]. - The model is built on the premise that human hand movements serve as the most complete execution template for various robotic end-effectors, allowing robots to benefit from human motion knowledge [6][10]. Group 3: Training Framework - The physical instruction tuning framework consists of three key components: pre-training from millions of human operation videos, physical space alignment to eliminate data source heterogeneity, and post-training for effective skill transfer to real robots [12][13][14]. - The framework addresses the challenges of data heterogeneity between 2D multimodal data and 3D robot action spaces, enhancing the model's ability to learn and generate actions [12]. Group 4: UniHand Dataset - The UniHand dataset, comprising over 150 million human hand gesture action samples, was systematically constructed to meet the training data needs of the physical instruction tuning framework [20][21]. - Even with just 2.5 million samples from this dataset, the model demonstrated significant performance improvements in gesture action prediction and real robot tasks [21]. Group 5: Experimental Validation - Comprehensive real robot experiments validated the effectiveness of the Being-H0 model, showing it outperformed both its base model InternVL3 and NVIDIA's GR00T N1.5 model in various tasks [22][24]. - The experiments confirmed that the data construction strategy significantly enhances the model's ability to learn human action knowledge from video data, leading to improved task success rates [24]. Group 6: Future Directions - The BeingBeyond team is focused on advancing core technologies in embodied intelligence, dexterous manipulation, and full-body motion control, aiming to integrate robots into everyday life [30].