Is Diversity All You Need for Scalable Robotic Manipulation?

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

This research investigates data diversity principles for scalable robotic manipulation, challenging the "more diverse is better" intuition across three dimensions. Experiments reveal that task diversity is more crucial than per-task demonstration quantity, enhancing transfer from diverse pre-training tasks to new scenarios. Surprisingly, multi-embodiment pre-training data is optional for cross-embodiment transfer; models trained on high-quality single-embodiment data, like AgiBot G1, can efficiently adapt to different platforms (e.g., Franka, Arx, Piper arms) and exhibit superior fine-tuning scaling properties compared to multi-embodiment pre-trained models (e.g., RDT-OXE). Furthermore, expert diversity, specifically velocity multimodality in human demonstrations, can confound policy learning. To address this, a distribution debiasing method is proposed, yielding GO-1-Pro, which achieves a 15% performance gain, equivalent to 2.5x more pre-training data. These findings offer practical guidance for effective robotic dataset scaling.

Key takeaway

For Robotics Engineers scaling manipulation datasets, you should prioritize task diversity over simply increasing per-task demonstration counts, as this significantly boosts transferability. If you are aiming for cross-embodiment generalization, consider that high-quality single-embodiment pre-training can be highly effective, potentially simplifying your data collection efforts. Crucially, implement velocity-based distribution debiasing on your expert demonstrations to mitigate confounding factors, which can yield substantial performance gains equivalent to 2.5x more data.

Key insights

Not all data diversity benefits robotic manipulation equally; task diversity and debiased expert data are key.

Principles

• Task diversity outweighs per-task demonstration quantity for transfer.
• Single-embodiment pre-training can achieve robust cross-embodiment transfer.
• Expert velocity multimodality can confound robot policy learning.

Method

A Velocity Model (VM) predicts expected robot velocity from observations. This is used to temporally rescale action chunks, debiasing velocity multimodality in demonstrations during policy training.

In practice

• Construct pre-training datasets with broad task diversity.
• Consider single-embodiment datasets for cross-embodiment generalization.
• Implement velocity debiasing for human demonstration data.

Topics

• Robotic Manipulation
• Data Diversity
• Scaling Laws
• Cross-Embodiment Transfer
• Distribution Debiasing
• Imitation Learning
• GO-1-Pro

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.