解决机器人操作中少数任务表现差的问题，提升长尾任务泛化能力。
Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation
- 提出APA增强方法，从头部任务向尾部任务迁移知识。
- 实验证明传统重采样无效，数据少会削弱空间推理能力。
- 适用于真实世界和仿真环境，适合研究长尾学习的学者。
通用机器人策略虽有望通过模仿学习掌握多样化操作技能，但其性能常受训练数据长尾分布的制约。由于数据集中在少数头部任务，模型在大量数据稀缺的尾部任务上泛化能力差。本文系统分析了这一长期存在的挑战，发现传统长尾学习策略（如重采样）无法有效提升尾部任务表现。进一步揭示，尾部任务数据稀缺直接损害了策略的空间推理能力。为此，提出一种简单有效的Approaching-Phase Augmentation（APA）方案，无需额外示范即可将头部任务的知识迁移至尾部任务。在仿真与真实机器人操作任务中的大量实验验证了APA的有效性。代码与演示已公开：https://mldxy.github.io/Project-VLA-long-tail/
原文摘要 · Abstract (English)
While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distribution of training demonstrations. Policies learned on such data, which is heavily skewed towards a few data-rich head tasks, frequently exhibit poor generalization when confronted with the vast number of data-scarce tail tasks. In this work, we conduct a comprehensive analysis of the pervasive long-tail challenge inherent in policy learning. Our analysis begins by demonstrating the inefficacy of conventional long-tail learning strategies (e.g., re-sampling) for improving the policy's performance on tail tasks. We then uncover the underlying mechanism for this failure, revealing that data scarcity on tail tasks directly impairs the policy's spatial reasoning capability. To overcome this, we introduce Approaching-Phase Augmentation (APA), a simple yet effective scheme that transfers knowledge from data-rich head tasks to data-scarce tail tasks without requiring external demonstrations. Extensive experiments in both simulation and real-world manipulation tasks demonstrate the effectiveness of APA. Our code and demos are publicly available at: https://mldxy.github.io/Project-VLA-long-tail/.
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