arXiv:2507.06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better". Throughout extensive experiments on vari
The paper addresses a critical gap in understanding data scaling for robotic manipulation, following significant successes in other AI domains like NLP and CV.
It challenges conventional wisdom on data diversity in robotics, potentially leading to more efficient and targeted data collection strategies crucial for practical robot deployment.
The focus shifts from merely 'more diverse data' to a nuanced understanding of task, embodiment, and expert diversity, impacting how robotic learning datasets are designed.
- · Robotics researchers
- · Robotics companies applying AI
- · AI data strategists
- · Inefficient data collectors
- · Generalized 'big data' approaches in robotics
Research in robotic data efficiency will accelerate, optimizing data collection strategies.
Reduced costs and faster development cycles for new robotic applications across various industries.
More rapid deployment of autonomous robotic systems into novel and complex environments due to robust and efficiently trained models.
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Read at arXiv cs.LG