Is Diversity All You Need for Scalable Robotic Manipulation?

Researchers challenge the 'more diversity is better' paradigm in robotic manipulation by demonstrating that task diversity matters more than data quantity, single-embodiment pre-training transfers effectively across platforms, and expert diversity can actually harm learning due to velocity multimodality. Their distribution debiasing method achieves 15% performance gains equivalent to 2.5x more pre-training data.

This research addresses a fundamental gap in understanding how to scale robotic manipulation datasets, moving beyond simplistic assumptions about data diversity that have driven success in NLP and computer vision. The study reveals that effective data scaling requires nuanced trade-offs rather than maximizing every dimension of diversity simultaneously.

The findings challenge conventional wisdom across three critical dimensions. Task diversity emerges as the primary driver of generalizable robot learning, suggesting that exposing models to varied manipulation tasks during pre-training creates more transferable representations than collecting numerous examples of the same task. This insight parallels recent trends in foundation model scaling but reveals domain-specific nuances. The discovery that high-quality single-embodiment data transfers effectively across robot platforms contradicts assumptions requiring multi-robot pre-training, reducing data collection costs substantially. Most surprisingly, expert diversity—arising from human demonstration variations—can degrade performance, with velocity multimodality identified as a key confounding factor.

For the robotics industry, these findings have immediate practical implications. Companies developing robotic systems can reduce pre-training dataset requirements by prioritizing task breadth and data quality over multi-platform collection, significantly accelerating development timelines and reducing costs. The proposed distribution debiasing method delivers efficiency gains equivalent to 2.5x more raw data, making robotics AI more economically viable. This research provides a roadmap for more efficient dataset construction that could accelerate commercialization of robotic manipulation systems across manufacturing, logistics, and service sectors. Future work should validate these principles at production scale and explore optimal debiasing approaches for different robot morphologies.

• →Task diversity matters more than per-task data quantity for effective robotic manipulation learning and transfer
• →Single-embodiment pre-trained models transfer effectively across different robot platforms without requiring multi-robot datasets
• →Expert diversity from human demonstrations can harm policy learning due to velocity multimodality, not improve it
• →Distribution debiasing of velocity ambiguity yields 15% performance gains equivalent to 2.5x additional pre-training data
• →Effective robotic dataset scaling requires targeted diversity strategies rather than maximizing all diversity dimensions