Is Diversity All You Need for Scalable Robotic Manipulation?
Authors: name: Modi Shi, name: Li Chen, name: Jin Chen, name: Yuxiang Lu, name: Chiming Liu, name: Guanghui Ren, name: Ping Luo, name: Di Huang, name: Maoqing Yao, name: Hongyang Li

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 various robot platforms, we reveal that (1) task diversity
proves more critical than per-task demonstration quantity, benefiting transfer
from diverse pre-training tasks to novel downstream scenarios; (2)
multi-embodiment pre-training data is optional for cross-embodiment
transfer-models trained on high-quality single-embodiment data can efficiently
transfer to different platforms, showing more desirable scaling property during
fine-tuning than multi-embodiment pre-trained models; and (3) expert diversity,
arising from individual operational preferences and stochastic variations in
human demonstrations, can be confounding to policy learning, with velocity
multimodality emerging as a key contributing factor. Based on this insight, we
propose a distribution debiasing method to mitigate velocity ambiguity, the
yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to
using 2.5 times pre-training data. Collectively, these findings provide new
perspectives and offer practical guidance on how to scale robotic manipulation
datasets effectively.