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

2025-08-08

AI Summary

Generalist robotic policies exhibit poor out-of-distribution (OOD) generalization, primarily due to shortcut learning—overreliance on task-irrelevant features. We identify two key data-level causes: insufficient intra-dataset diversity and inter-dataset distributional discrepancies—termed “dataset fragmentation”—within large-scale robot datasets (e.g., Open X-Embodiment). Method: To mitigate this, we propose an offline data augmentation framework grounded in theoretical analysis and validated through simulation and real-robot experiments. Contribution/Results: Our approach effectively suppresses shortcut learning, significantly improving the OOD generalization of generalist policies (e.g., π₀). It yields measurable gains in zero-shot task transfer across heterogeneous robot platforms and task domains. Crucially, it provides actionable, empirically grounded guidelines for curating high-quality, robust robotic datasets—advancing both data-centric and policy-centric paradigms in embodied AI.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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., $π_0$, in both simulation and real-world environments. More information at https://lucky-light-sun.github.io/proj/shortcut-learning-in-grps/.

Problem

• Investigating shortcut learning in generalist robot policies
• Identifying dataset diversity and fragmentation causes
• Proposing strategies to enhance generalization capabilities

Innovation

• Dataset diversity enhancement reduces shortcut learning
• Distributional disparities mitigation improves generalization capability
• Robotic data augmentation strategies counter dataset fragmentation