Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation

Youguang Xing, Xu Luo, Junlin Xie, Lianli Gao, Heng Tao Shen, Jingkuan Song
Proceedings of The 9th Conference on Robot Learning, PMLR 305:3239-3266, 2025.

Abstract

Generalist robot policies trained on large-scale datasets such as Open X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks. However, they often struggle to generalize beyond the distribution of their training data. In this paper, we investigate the underlying cause of this limited generalization capability. We identify shortcut learning—the reliance on task-irrelevant features—as a key impediment to generalization. Through comprehensive theoretical and empirical analysis, we uncover two primary contributors to shortcut learning: (1) limited diversity within individual sub-datasets, and (2) significant distributional disparities across sub-datasets, leading to dataset fragmentation. These issues arise from the inherent structure of large-scale datasets like OXE, which are typically composed of multiple sub-datasets collected independently across varied environments and embodiments. Our findings provide critical insights into dataset collection strategies that can reduce shortcut learning and enhance the generalization ability of generalist robot policies. Moreover, in scenarios where acquiring new large-scale data is impractical, we demonstrate that carefully selected robotic data augmentation strategies can effectively reduce shortcut learning in existing offline datasets, thereby improving generalization capabilities of generalist robot policies, e.g., $\pi_0$ in the SIMPLER Environment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-xing25a, title = {Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation}, author = {Xing, Youguang and Luo, Xu and Xie, Junlin and Gao, Lianli and Shen, Heng Tao and Song, Jingkuan}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {3239--3266}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/xing25a/xing25a.pdf}, url = {https://proceedings.mlr.press/v305/xing25a.html}, abstract = {Generalist robot policies trained on large-scale datasets such as Open X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks. However, they often struggle to generalize beyond the distribution of their training data. In this paper, we investigate the underlying cause of this limited generalization capability. We identify shortcut learning—the reliance on task-irrelevant features—as a key impediment to generalization. Through comprehensive theoretical and empirical analysis, we uncover two primary contributors to shortcut learning: (1) limited diversity within individual sub-datasets, and (2) significant distributional disparities across sub-datasets, leading to dataset fragmentation. These issues arise from the inherent structure of large-scale datasets like OXE, which are typically composed of multiple sub-datasets collected independently across varied environments and embodiments. Our findings provide critical insights into dataset collection strategies that can reduce shortcut learning and enhance the generalization ability of generalist robot policies. Moreover, in scenarios where acquiring new large-scale data is impractical, we demonstrate that carefully selected robotic data augmentation strategies can effectively reduce shortcut learning in existing offline datasets, thereby improving generalization capabilities of generalist robot policies, e.g., $\pi_0$ in the SIMPLER Environment.} }
Endnote
%0 Conference Paper %T Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation %A Youguang Xing %A Xu Luo %A Junlin Xie %A Lianli Gao %A Heng Tao Shen %A Jingkuan Song %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-xing25a %I PMLR %P 3239--3266 %U https://proceedings.mlr.press/v305/xing25a.html %V 305 %X Generalist robot policies trained on large-scale datasets such as Open X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks. However, they often struggle to generalize beyond the distribution of their training data. In this paper, we investigate the underlying cause of this limited generalization capability. We identify shortcut learning—the reliance on task-irrelevant features—as a key impediment to generalization. Through comprehensive theoretical and empirical analysis, we uncover two primary contributors to shortcut learning: (1) limited diversity within individual sub-datasets, and (2) significant distributional disparities across sub-datasets, leading to dataset fragmentation. These issues arise from the inherent structure of large-scale datasets like OXE, which are typically composed of multiple sub-datasets collected independently across varied environments and embodiments. Our findings provide critical insights into dataset collection strategies that can reduce shortcut learning and enhance the generalization ability of generalist robot policies. Moreover, in scenarios where acquiring new large-scale data is impractical, we demonstrate that carefully selected robotic data augmentation strategies can effectively reduce shortcut learning in existing offline datasets, thereby improving generalization capabilities of generalist robot policies, e.g., $\pi_0$ in the SIMPLER Environment.
APA
Xing, Y., Luo, X., Xie, J., Gao, L., Shen, H.T. & Song, J.. (2025). Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:3239-3266 Available from https://proceedings.mlr.press/v305/xing25a.html.

Related Material