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Title: Agibot Groundbreaking Release - New Perspectives on Task Embodiment and Expert Data Diversity

China, 17th Oct 2025 — Recently, a research team jointly formed by Agibot, Chuangzhi Academy, The University of Hong Kong, and others, published a breakthrough study that systematically explores three key dimensions of data diversity in robot manipulation learning: task diversity, robot embodiment diversity, and expert diversity. This research challenges the traditional belief in robotics learning that "more diverse data is always better," providing new theoretical guidance and practical pathways for building scalable robot operating systems.Task Diversity: Specialist or Generalist? Data Provides the Answer A core question has long perplexed researchers in robot learning: when training a robot model, whether to focus on data highly relevant to the target task for "specialist" training, or to collect data from various tasks for a "generalist" learning approach.To answer this, a clever comparative experiment was designed, constructing two pre-training datasets based on the AgiBot World dataset with identical sizes but drastically different task distributions:"Specialist" Dataset (Task Sampling) – 10% of tasks most relevant to the target tasks were carefully selected, all containing the five core atomic skills required for evaluation: pick, place, grasp, pour, and fold. As shown in the figures, this strategy, while having lower skill diversity, is highly concentrated on the skills needed for the downstream tasks."Generalist" Dataset (Trajectory Sampling) – 10% of trajectories from each task were randomly sampled, preserving the full task diversity spectrum of the original dataset. Although this approach resulted in fewer trajectories directly related to the target skills (59.2% vs. 71.1%), it achieved a more balanced skill distribution.The results revealed an une...

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