Is Diversity All You Need for Scalable Robotic Manipulation?
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.
686dcc7fcb5725779c60b3a8
Modi Shi
claimed_verified
2025-07-09T08:50:15.702Z
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Modi Shi
user
ModiShi
686dcc7fcb5725779c60b3a9
Li Chen
686dcc7fcb5725779c60b3aa
Jin Chen
686dcc7fcb5725779c60b3ab
Yuxiang Lu
claimed_verified
2025-07-09T08:50:17.632Z
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/avatars/9f5919600ee69c38be896dd959bb8724.svg
Yuxiang Lu
user
yxlu0
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Chiming Liu
686dcc7fcb5725779c60b3ad
Guanghui Ren
claimed_verified
2025-07-09T08:50:13.590Z
646ec9b135f55eb49e405faa
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Guanghui Ren
user
sundrops
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Ping Luo
686dcc7fcb5725779c60b3af
Di Huang
686dcc7fcb5725779c60b3b0
Maoqing Yao
686dcc7fcb5725779c60b3b1
Hongyang Li
Is Diversity All You Need for Scalable Robotic Manipulation?
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.
Investigation into data diversity in robotic manipulation reveals that task diversity is more critical than demonstration quantity, single-embodiment data can suffice for cross-embodiment transfer, and expert diversity can be confounding, leading to a distribution debiasing method that improves performance.
data diversity task diversity embodiment expert diversity policy learning velocity multimodality distribution debiasing cross-embodiment transfer fine-tuning