Is Diversity All You Need for Scalable Robotic Manipulation?
A new research paper explores the optimal scaling of data for robotic manipulation tasks, challenging the common assumption that more diverse data is always better. The study found that task diversity is more crucial than the quantity of demonstrations for a single task, and that pre-training on a single robot embodiment can be sufficient for transferring to new platforms. Furthermore, the research indicates that diversity in expert demonstrations can sometimes hinder policy learning, proposing a debiasing method to improve performance. AI
IMPACT Provides practical guidance on optimizing data collection for robotic learning, potentially accelerating development and deployment.